TORTINI

For your delectation and delight, desultory dicta on the law of delicts.

The Faux Bayesian Approach in Litigation

July 13th, 2022

In an interesting series of cases, an expert witness claimed to have arrived at the specific causation of plaintiff’s stomach cancer by using “Bayesian probabilities which consider the interdependence of individual probabilities.” Courtesy of counsel in the cases, I have been able to obtain a copy of the report of the expert witness, Dr. Robert P. Gale. The cases in which Dr. Gale served were all FELA cancer cases against the Union Pacific Railroad, brought for cancers diagnosed in the plaintiffs. Given his research and writings in hematopoietic cancers and molecular biology, Dr. Gale would seem to have been a credible expert witness for the plaintiffs in their cases.[1]

The three cases involving Dr. Gale were all decisions on Rule 702 motions to exclude his causation opinions. In all three cases, the court found Dr. Gale to be qualified to opine on causation, which finding is decided by a very low standard in federal court. In two of the cases, the same judge, federal Magistrate Judge Cheryl R. Zwart, excluded Dr. Gale’s opinions.[2] In at least one of the two cases, the decision seemed rather straightforward, given that Dr. Gale claimed to have ruled out alternative causes of Mr. Hernandez’s stomach cancer.  Somehow, despite his qualifications, however, Dr. Gale missed that Mr. Hernandez had had helicobacter pylori infections before he was diagnosed with stomach cancer.

In the third case, the district judge denied the Rule 702 motion against Dr. Gale, in a cursory, non-searching review.[3]

The common thread in all three cases is that the courts dutifully noted that Dr. Gale had described his approach to specific causation as involving “Bayesian probabilities which consider the interdependence of individual probabilities.” The judicial decisions never described how Dr. Gale’s invocation of Bayesian probabilities contributed to his specific causation opinion, and a careful review of Dr. Gale’s report reveals no such analysis. To be explicit, there was no discussion of prior or posterior probabilities or odds, no discussion of likelihood ratios, or Bayes factors. There was absolutely nothing in Dr. Gale’s report that would warrant his claim that he had done a Bayesian analysis of specific causation or of the “interdependence of individual probabilities” of putative specific causes.

We might forgive the credulity of the judicial officers in these cases, but why would Dr. Gale state that he had done a Bayesian analysis? The only reason that suggests itself is that Dr. Gale was bloviating in order to give his specific causation opinions an aura of scientific and mathematical respectability. Falsus in duo, falsus in omnibus.[4]


[1] See, e.g., Robert Peter Gale, et al., Fetal Liver Transplantation (1987); Robert Peter Gale & Thomas Hauser, Chernobyl: The Final Warning (1988); Kenneth A. Foon, Robert Peter Gale, et al., Immunologic Approaches to the Classification and Management of Lymphomas and Leukemias (1988); Eric Lax & Robert Peter Gale, Radiation: What It Is, What You Need to Know (2013).

[2] Byrd v. Union Pacific RR, 453 F.Supp.3d 1260 (D. Neb. 2020) (Zwart, M.J.); Hernandez v. Union Pacific RR, No. 8: 18CV62 (D. Neb. Aug. 14, 2020).

[3] Langrell v. Union Pacific RR, No. 8:18CV57, 2020 WL 3037271 (D. Neb. June 5, 2020) (Bataillon, S.J.).

[4] Dr. Gale’s testimony has not fared well elsewhere. See, e.g., In re Incretin-Based Therapies Prods. Liab. Litig., 524 F.Supp.3d 1007 (S.D. Cal. 2021) (excluding Gale); Wilcox v. Homestake Mining Co., 619 F. 3d 1165 (10th Cir. 2010); June v. Union Carbide Corp., 577 F. 3d 1234 (10th Cir. 2009) (affirming exclusion of Dr. Gale and entry of summary judgment); Finestone v. Florida Power & Light Co., 272 F. App’x 761 (11th Cir. 2008); In re Rezulin Prods. Liab. Litig., 309 F.Supp.2d 531 (S.D.N.Y. 2004) (excluding Dr. Gale from offering ethical opinions).

Small Relative Risks and Causation (General & Specific)

June 28th, 2022

The Bradford Hill Predicate: Ruling Out Random and Systematic Error

In two recent posts, I spent some time discussing a recent law review, which had some important things to say about specific causation.[1] One of several points from which I dissented was the article’s argument that Sir Austin Bradford Hill had not made explicit that ruling out random and systematic error was required before assessing his nine “viewpoints” on whether an association was causal. I take some comfort in the correctness of my interpretation of Sir Austin’s famous article by reading the analysis of no less than Sir Richard Doll’s own analysis of his friend and colleague’s views:

“In summary, we have to show, first, that the association cannot reasonably be explained by chance (bearing in mind that extreme chances do turn up from time to time or no one would buy a ticket in a national lottery), by methodological bias (which can have many sources), or by confounding (which needs to be explored but should not be postulated without some idea of what it might be). Second, we have to see whether the available evidence gives positive support to the concept of causality: that is to say, how it matches up to Hill’s (1965) guidelines (Table 1).”[2]

On the issue of whether small relative risks can establish general causation, the Differential Etiology  paper urged caution in interpreting results when “strength of a relationship is modest.” The strength of an association is, of course, one of the nine Bradford Hill viewpoints, which come into play after we have a “clear-cut” association beyond what we would care to attribute to chance. Additionally, strength of association is primarily a quantitive assessment, and the advice given about caution in the face of “modest” associations is not terribly helpful.  The scientific literature does better.

Sir Richard’s 2002 paper is in a sense a scientific autobiography about some successes in discerning causal associations from observational studies. Unlike expert witnesses for the lawsuit industry, Sir Richard’s essay is notably for its intellectual humility.  In addition to its clear and explicit articulation of the need to rule out random and systematic error before proceeding to a consideration of Sir Austin’s nine guidelines, Sir Richard Doll’s 2002 essay is instructive for judges and lawyers, for other reasons. For example, he raises and explains the problem encountered for causal inference by small relative risks:

“Small relative risks of the order of 2:1 or even less are what are likely to be observed, like the risk now recorded for childhood leukemia and exposure to magnetic fields of 0.4 µT or more (Ahlbom et al. 2000) that are seldom encountered in the United Kingdom. And here the problems of eliminating bias and confounding are immense.”[3]

Sir Richard opines that relative risks under two can be shown to be causal associations, but often with massive data, randomization, and a good deal of support from experimental work.

Another Sir Richard, Sir Richard Peto, along with Sir Richard Doll, raised this concern in their classic essay on the causes of cancer, where they noted that relative risks between one and two create extremely difficult problems of interpretation because the role of the association cannot be confidently disentangled from the contribution of biases.[4] Small relative risks are thus seen as raising a concern about bias and confounding.[5]

In the legal world, courts have recognized that the larger the relative risk, or the strength of association, the more likely a general causation inference can be drawn, even when they blithely ignored the role of actual or residual confounding.[6]

The chapters on statistics and on epidemiology in the current (third) edition of the Reference Manual on Scientific Evidence directly tie the magnitude of the association to the elimination of confounding as an alternative explanation for causality of an association. A larger “effect size,” such as for smoking and lung cancer (greater than ten-fold, and often higher than 30-fold), eliminates the need to worry about confounding:

“Many confounders have been proposed to explain the association between smoking and lung cancer, but careful epidemiological studies have ruled them out, one after the other.”[7]

*  *  *  *  *  *

“A relative risk of 10, as seen with smoking and lung cancer, is so high that it is extremely difficult to imagine any bias or confounding factor that might account for it. The higher the relative risk, the stronger the association and the lower the chance that the effect is spurious. Although lower relative risks can reflect causality, the epidemiologist will scrutinize such associations more closely because there is a greater chance that they are the result of uncontrolled confounding or biases.”[8]

The Reference Manual omits the converse: the lower relative risk, the weaker the association and the greater the chance that the apparent effect is spurious. The authors’ intent, however, is clear enough. In the Appendix, below, I have collected some pronouncements from the scientific literature that urge caution in drawing causal inferences in the face of weak associations, but with more quantitative guidance.

 Small RRs and Specific Causation

Sir Richard Doll was among the first generation of epidemiologists in the academic world. He eschewed the use of epidemiology for discerning the cause of an individual’s disease:

“That asbestos is a cause of lung cancer in this practical sense is incontrovertible, but we can never say that asbestos was responsible for the production of the disease in a particular patient, as there are many other etiologically significant agents to which the individual may have been exposed, and we can speak only of the extent to which the risk of the disease was increased by the extent of his or her exposure.”[9]

On the individual attribution issue, Sir Richard’s views do not hold up as well as his analysis of general causation. Epidemiologic study results are used to predict future disease in individuals, to guide screening and prophylaxis decisions, to determine pharmacologic and surgical interventions in individuals, and to provide prognoses to individuals. Just as confounding falls by the wayside in the analysis of general causation with relative risks greater than 20, so too do the concerns about equating increased risk with specific causation.

The urn model of probability, however, gives us some insight into attributability. If we expected 100 cases of a disease in a sample of a certain size, but we observed 200 cases, then we would have 100 expected and 100 excess cases. Attribution would be no better than a flip of a coin.  If, however, in a situation where the relative risk was 20, we might have 100 expected cases and 2,000 excess cases. The odds of a given case’s being an excess case are rather strong, and even the agnostics and dissenters from probabilistic reasoning in individual cases become weak kneed about denying recovery when the claimant is similar to the cases seen in the study sample.

******************Appendix*************************

Norman E. Breslow & N. E. Day, “Statistical Methods in Cancer Research,” in The Analysis of Case-Control Studies 36 (IARC Pub. No. 32, 1980) (“[r]elative risks of less than 2.0 may readily reflect some unperceived bias or confounding factor”)

Richard Doll & Richard Peto, The Causes of Cancer 1219 (1981) (“when relative risk lies between 1 and 2 … problems of interpretation may become acute, and it may be extremely difficult to disentangle the various contributions of biased information, confounding of two or more factors, and cause and effect.”)

Iain K. Crombie, “The limitations of case-control studies in the detection of environmental carcinogens,” 35 35 J. Epidem. & Community Health 281, 281 (1981) (“The case-control study is unable to detect very small relative risks (< 1.5) even where exposure is widespread and large numbers of cases of cancer are occurring in the population.”)

Ernst L. Wynder & Geoffrey C. Kabat, “Environmental Tobacco Smoke and Lung Cancer: A Critical Assessment,” in H. Kasuga, ed., Indoor Air Quality 5, 6 (1990) (“An association is generally considered weak if the odds ratio is under 3.0 and particularly when it is under 2.0, as is the case in the relationship of ETS and lung cancer. If the observed relative risk is small, it is important to determine whether the effect could be due to biased selection of subjects, confounding, biased reporting, or anomalies of particular subgroups.”).

Ernst L. Wynder, “Epidemiological issues in weak associations,” 19 Internat’l  J. Epidemiol. S5 (1990)

David Sackett, R. Haynes, Gordon Guyatt, and Peter Tugwell, Clinical  Epidemiology: A Basic Science for Clinical Medicine (2d ed. 1991)

Muin J. Khoury, Levy M. James, W. Dana Flanders, and David J. Erickson, “Interpretation of recurring weak associations obtained from epidemiologic studies of suspected human teratogens,” 46 Teratology 69 (1992);

Lynn Rosenberg, “Induced Abortion and Breast Cancer: More Scientific Data Are Needed,” 86 J. Nat’l Cancer Instit. 1569, 1569 (1994) (“A typical difference in risk (50%) is small in epidemiologic terms and severely challenges our ability to distinguish if it reflects cause and effect or if it simply reflects bias.”) (commenting upon Janet R. Daling, K. E. Malone, L. F. Voigt, E. White, and Noel S. Weiss, “Risk of breast cancer among young women: relationship to induced abortion,” 86 J. Nat’l Cancer Inst. 1584 (1994);

Linda Anderson, “Abortion and possible risk for breast cancer: analysis and inconsistencies,” (Wash. D.C., Nat’l Cancer Institute, Oct. 26,1994) (“In epidemiologic research, relative risks of less than 2 are considered small and are usually difficult to interpret. Such increases may be due to chance, statistical bias, or effects of confounding factors that are sometimes not evident.”); 

Washington Post (Oct. 27, 1994) (quoting Dr. Eugenia Calle, Director of Analytic Epidemiology for the American Cancer Society: “Epidemiological studies, in general are probably not able, realistically, to identify with any confidence any relative risks lower than 1.3 (that is a 30% increase in risk) in that context, the 1.5 [reported relative risk of developing breast cancer after abortion] is a modest elevation compared to some other risk factors that we know cause disease.”)

Gary Taubes, “Epidemiology Faces Its Limits,” 269 Science 164, 168 (July 14, 1995) (quoting Marcia Angell, former editor of the New England Journal of Medicine, as stating that “[a]s a general rule of thumb, we are looking for a relative risk of 3 or more [before accepting a paper for publication], particularly if it is biologically implausible or if it’s a brand new finding.”) (quoting John C. Bailar: “If you see a 10-fold relative risk and it’s replicated and it’s a good study with biological backup, like we have with cigarettes and lung cancer, you can draw a strong inference. * * * If it’s a 1.5 relative risk, and it’s only one study and even a very good one, you scratch your chin and say maybe.”)

Samuel Shapiro, “Bias in the evaluation of low-magnitude associations: an empirical perspective,” 151 Am. J. Epidemiol. 939 (2000)

David A. Freedman & Philip B. Stark, “The Swine Flu Vaccine and Guillain-Barré Syndrome: A Case Study in Relative Risk and Specific Causation,” 64 Law & Contemp. Probs. 49, 61 (2001) (“If the relative risk is near 2.0, problems of bias and confounding in the underlying epidemiologic studies may be serious, perhaps intractable.”).

S. Straus, W. Richardson, P. Glasziou, and R. Haynes, Evidence-Based Medicine. How to Teach and Practice EBM (3d ed. 2005)

David F. Goldsmith & Susan G. Rose, “Establishing Causation with Epidemiology,” in Tee L. Guidotti & Susan G. Rose, eds., Science on the Witness Stand: Evaluating Scientific Evidence in Law, Adjudication, and Policy 57, 60 (2001) (“There is no clear consensus in the epidemiology community regarding what constitutes a ‘strong’ relative risk, although, at a minimum, it is likely to be one where the RR is greater than two; i.e., one in which the risk among the exposed is at least twice as great as among the unexposed.”)

Samuel Shapiro, “Looking to the 21st century: have we learned from our mistakes, or are we doomed to compound them?” 13 Pharmacoepidemiol. & Drug Safety  257 (2004)

Mark Parascandola, Douglas L Weed & Abhijit Dasgupta, “Two Surgeon General’s reports on smoking and cancer: a historical investigation of the practice of causal inference,” 3 Emerging Themes in Epidemiol. 1 (2006)

Heinemann, “Epidemiology of Selected Diseases in Women,” chap. 4, in M.A. Lewis, M. Dietel, P.C. Scriba, W.K. Raff, eds., Biology and Epidemiology of Hormone Replacement Therapy 47, 48 (2006) (discussing the “small relative risks in relation to bias/confounding and causal relation.”)

Roger D. Peng, Francesca Dominici, and Scott L. Zeger, “Reproducible Epidemiologic Research,” 163 Am. J. Epidem. 783, 784 (2006) (“The targets of current investigations tend to have smaller relative risks that are more easily confounded.”)

R. Bonita, R. Beaglehole & T. Kjellström, Basic Epidemiology 93 (W.H.O. 2d ed. 2006) (“A strong association between possible cause and effect, as measured by the size of the risk ratio (relative risk), is more likely to be causal than is a weak association, which could be influenced by confounding or bias. Relative risks greater than 2 can be considered strong.”)

David A. Grimes & Kenneth F. Schulz, “False alarms and pseudo-epidemics: the limitations of observational epidemiology,” 120 Obstet. & Gynecol. 920 (2012) (“Most reported associations in observational clinical research are false, and the minority of associations that are true are often exaggerated. This credibility problem has many causes, including the failure of authors, reviewers, and editors to recognize the inherent limitations of these studies. This issue is especially problematic for weak associations, variably defined as relative risks (RRs) or odds ratios (ORs) less than 4.”)

Kenneth F. Schulz & David A. Grimes, Essential Concepts in Clinical Research:
Randomised Controlled Trials and Observational Epidemiology at 75 (2d ed. 2019) (“Even after attempts to minimise selection and information biases and after control for known potential confounding factors, bias often remains. These biases can easily account for small associations. As a result, weak associations (which dominate in published studies) must be viewed with circumspection and humility.43 Weak associations, defined as relative risks between 0.5 and 2.0, in a cohort study can readily be accounted for by residual bias (Fig. 7.2). Because case-control studies are more susceptible to bias than are cohort studies, the bar must be set higher. ln case-control studies, weak associations can be viewed as odds ratios between 0.33 and 3.0 (Fig. 7.3). Results that full within these zones may be due to bias. Results that full outside these bounds in either direction may deserve attention.”)

Brian L. Strom, “Basic Principles of Clinical Epidemiology Relevant to Pharmacoepidemiologic Studies,” chap. 3, in Brian L. Strom, Stephen E. Kimmel & Sean Hennessy, eds., Pharmacoepidemiology 48 (6th ed. 2020) (“Conventionally, epidemiologists consider an association with a relative risk of less than 2.0 a weak association.”)


[1] Joseph Sanders, David L. Faigman, Peter B. Imrey, and Philip Dawid, “Differential Etiology: Inferring Specific Causation in the Law from Group Data in Science,” 63 Ariz. L. Rev. 851 (2021) [Differential Etiology].

[2] Richard Doll, “Proof of Causality: deduction from epidemiological observation,” 45 Persp. Biology & Med. 499, 501 (2002) (emphasis added).

[3] Id. at 512.

[4] Richard Doll & Richard Peto, The Causes of Cancer 1219 (1981) (“when relative risk lies between 1 and 2 … problems of interpretation may become acute, and it may be extremely difficult to disentangle the various contributions of biased information, confounding of two or more factors, and cause and effect.”).

[5]Confounding in the Courts” (Nov. 2, 2018); “General Causation and Epidemiologic Measures of Risk Size” (Nov. 24, 2012). 

[6] See King v. Burlington Northern Santa Fe Railway Co., 762 N.W.2d 24, 40 (Neb. 2009) (“the higher the relative risk, the greater the likelihood that the relationship is causal”); Landrigan v. Celotex Corp., 127 N.J. 404, 605 A.2d 1079, 1086 (1992) (“The relative risk of lung cancer in cigarette smokers as compared to nonsmokers is on the order of 10:1, whereas the relative risk of pancreatic cancer is about 2:1. The difference suggests that cigarette smoking is more likely to be a causal factor for lung cancer than for pancreatic cancer.”).

[7] RMSE3d at 219.

[8] RMSE3d at 602. 

[9] Richard Doll, “Proof of Causality: deduction from epidemiological observation,” 45 Persp. Biology & Med. 499, 500 (2002).

Differential Etiologies – Part Two – Ruling Out

June 19th, 2022

Perhaps the most important point of this law review article, “Differential Etiology: Inferring Specific Causation in the Law from Group Data in Science,”  is that general causation is necessary but insufficient, standing alone, to show specific causation. To be sure, the authors proclaimed that strong evidence of general causation somehow reduces the burden to show specific causation, but this pronouncement turned out to be an ipse dixit, without supporting analysis or citation. On general causation itself, what the authors characterized as the “ruling in” part of differential etiology, the authors offered some important considerations for courts to consider. Not the least of the important advice on general causation was urging caution in interpreting results when “strength of a relationship is modest.”[1] Given that they were talking to judges and lawyers, the advice might have taken on greater saliency if the authors explicitly noted that modest strength of a putative relationship means small relative risks, such as those smaller than two or three.

Acute Onset Conditions

The authors’ stated goal of bringing clarity to the determination of differential-etiology is a laudable one. In seeking clarity, they brush away some “easy” cases, such as the causal determination of acute onset conditions. Even so, the authors do not give any concrete examples. A broken bone discovered immediately after a car crash would hardly give a court much pause, but something such as the onset of acute liver failure shortly after ingesting a new medication turns out to be much more complicated than many would anticipate. Viral infections and autoimmune disease must be eliminated, and so such events are clearly in the realm of differential etiology, despite the close temporal proximity.

So-Called Signature Diseases

The authors also try to brush aside the “easy” case of signature diseases as not requiring differential etiology. The complexity of such cases ultimately embarrasses everyone. The authors no doubt thought that they were on safe ground in proffering the example of mesothelioma as a signature cancer caused by only asbestos (without wading into the deeper complexity of what is asbestos and which minerals in what mineralogical habit actually cause the disease).[2] Unfortunately, mesothelioma has never been a truly signal disease. The authors nonetheless consider it as one, with the caveat that mesotheliomas not caused by asbestos are “very rare.” And what was the authority for this statement? The Pennsylvania Supreme Court! Now the Pennsylvania Supreme Court is no doubt, at times, an authority on Pennsylvania law, if only because the Court is the last word on this contorted body of law. The Justices of that Court, however, would probably be the first to disclaim any credibility on the causes of any disease.[3]

The authors further distort the notion of signature diseases by stating that “[v]aginal adenocarcinoma in young women appears to be a signature disease associated with maternal use of DES.”[4] This cannot be right because over 10% of vaginal cancers are adenocarcimas. The principle of charity requires us to assume that the authors meant to indicate clear cell vaginal adenocarcinoma, but even so, charity will not correct the mistake. DES daughters do indeed have an increased risk of developing developing clear cell adenocarcinoma, but this type of cancer was well described before DES was ever invented and prescribed to women.[5]

Perhaps the safest ground for signature diseases is in microbiology, where we have infectious disease defined by the microbial agent that is uniquely associated with the disease. Probably close to the infectious diseases are the nutritional deficiency diseases defined by the absence of an essential nutrient or vitamin. To be sure, there are non-infectious diseases such as the pneumoconioses, each defined by the nature of the inhaled particle. Contrary to the authors’ contention, these diseases no not necessarily remove differential etiology from the analysis. Silicosis has a distinctive radiographic appearance, and yet that radiographic appearance is the same in many cases of coccidioidomycosis (Valley Fever). Asbestosis has a different radiographic appearance of the lungs and pleura, but the radiographic patterns might well be confused with the sequelae of rheumatoid arthritis or other interstitial lung diseases. At low levels of profusion of radiographic opacities, diseases such asbestosis and silicosis have diagnostic criteria that are far from perfect sensitivity and specificity. In one of the very first asbestos cases I defended, the claimant was diagnosed, by no less than the late Dr. Irving Selikoff,[6] with asbestosis, 3/3 on the ILO scale of linear, irregular radiographic lung opacities. An autopsy, however, found that there was no asbestosis at all, or even an elevated tissue fiber burden; the claimant had died of bilateral lymphangenitic carcinomatosis.

Definitive Mechanistic Pathway to Individual Causation

The paper presents a limited discussion of genetic causation. In the instance of mutations of highly penetrant alleles, identifying the genetic mutation will provide the general and the specific cause in a case. The authors also acknowledge that there may be cases involving hypothetical biomarkers that reveals a well-documented causal pathway from exposure to disease.

Differential Etiologies

So what happens when the plaintiff is claiming that he has developed a disease of ordinary life, one that has multiple known causes? Disease onset is not acute, but rather after a lengthy latency period. The plaintiff wants to inculpate the supposedly tortious exposure (the tortogen), and avoid the conclusion that any or all of the known alternative causes participated in his case. If there are cases of the disease without known causes (idiopathogens), the claimant will need to exclude idiopathogens in favor of fingering the tortogen as responsible for his bad outcome.

The authors helpfully distinguish differential diagnosis from differential etiology. The confusion of the two concepts has led to courts’ generally over-endorsing the black box of clinical judgment in health effects litigation. At the very least, this article can perhaps help the judiciary to move on from this naïve confusion.[7]

The authors advance the vague notion that somehow “clinical information” can supplement a relative that is not greater than two to augment the specific causation inference. This was, to be sure, the assertion of the New Jersey Supreme Court, based upon the improvident concession of the defense lawyer who argued the case.[8] There was nothing in the record of the New Jersey case, however, that would support the relevance of clinical information to the causal analysis of the plaintiff’s colorectal cancer.

The authors also point to a talc ovarian cancer case as exemplifying the use of clinical data to supplement a relative risk below two.[9] The cited case, however, involved expert witnesses who claimed a relative risk greater than two for the tortogen, and who failed to show how clinical information (such as the presence of talc in ovarian tissue) made the claimant any more likely to have had a cancer caused by talc.

Adverting to “clinical information” to supplement the relative risk all-too-often is hand waving that offers no analytical support for the specific causal inference. The clinical factors often are covariates in the multivariate model that generated the relevant relative risk. As such, the relative risk represents an assessment of the strength of the relevant association, independent of the clinical factors that are captured in the co-variates, in the multivariate model.  In the New Jersey case, Landrigan, plaintiff had no asbestosis that would suggest he even had a serious exposure to asbestos. In a companion case, Caternicchio, the plaintiff claimed that he had asbestosis, and somehow this made the causal inference for his colorectal cancer stronger.[10]  The epidemiologic studies he relied upon, however, stratified their analyses by length of exposure, and by radiographic category of asbestosis, neither of which suggested any relationship between radiographic findings and colorectal cancer outcome.

Perhaps because the authors are academics, they had to ask questions no one has every raised in a serious way in litigation, such as whether in addition to the clinical information, claimants could assert that toxicological data could be used to supplement a low (not greater than two) relative risk. The authors state the obvious; namely, toxicologic evidence is best suited to the assessment of general causation. They do not stop there, as they might have. Throwing their stated task of explicating the scientific foundations for specific causation inferences to the wind, the authors tell us that “[t]here is no formula for when such toxicologic evidence can tip the scales on the question of specific causation.”[11] And they wind up telling us vacuously that if the relevant epidemiology showed a small effect size, such as a two percent increased risk (RR = 1.02), then it would be unclear “how any animal data could cause one to substantially alter the best estimate of a human effect to reach a more-likely-than-not threshold.”[12] At this point in their paper, the authors seem to be discussing specific causation, but they offer nothing in the way of scientific evidence or examples of how toxicologic data could supplement a low relative risk (less than or equal to two) to permit a specific causation inference.

Idiopathy

When the analysis of the putative risk is done in a multivariate model that fairly covers the other relevant risks, relative risks less than 100 or so, suggest that there is a substantial baseline or background risk for the outcome of concern. When the relative risks identified in such analyses are less than 5 or so, the studies will suggest a reasonable proportion of so-called background cases with idiopathic (unknown) causes. Differential etiologies will have to rule out those mysterious idiopathogens.

If the putative specific cause is the only substance established to cause the outcome of concern, and the RR is greater than 1.0 and less than or equal to 2.0, by definition, there is a large base rate of the disease. No amount of hokey pokey will rule out the background causes. The authors deal with this scenario under the heading of differential etiology in the face of idiopathic causes, and characterize it as a “problem.”

Long story short, the authors conclude that “perhaps it is reasonable for courts to disregard idiopathic causes in those cases where idiopathic causes comprise a relatively small percent of all injuries.”[13] Such cases, however, by definition will diseases for which most causes are known, and the attributable fractions collectively for the known risks will be very high (say greater than 80 or 90%). Conversely, when the attributable fraction for all known risks is lower than 80%, the unexplained portion of the disease cases will represent idiopathic cases and causes that cannot be rule out with any confidence.

Differential etiology cannot work in the situation with a substantial baseline risk because there will be a disjunct (idiopathogen(s)) in the first statement of the syllogism, which cannot be ruled out. Thus, even if every other putative cause can be eliminated, the claimant will be left with the either the tortogen or the baseline risk as the cause of his injury, and the claimant will never arrive at a conclusion that is free of a disjunction that precludes judgment in his favor. In this scenario, the claimant must lose as a matter of law.

In their discussion of this issue, the authors note that this indeterminancy resulted in the exclusion of plaintiff’s expert witnesses in the notorious case of Milward v. Acuity Specialty Products Group, Inc.[14] In Milward, plaintiff had developed a rare variety of acute myeloid leukemia (AML), which had a large attributable fraction for idiopathic causation. This factual setting simply means that no known cause exists with a large relative risk, or even a small relative risk of 1.3 or so. Remarkably, these authors state that Milward “had prevailed on the general-causation issue” but in fact, no trial was ever held. The defense prevailed at the trial court by way of Rule 702 exclusion of plaintiff’s causation expert witnesses, but the First Circuit reversed and remanded for trial. The only prevailing that took place was the questionable avoidance of exclusion and summary judgment.[15]

On remand, the defense moved again to exclude plaintiffs’ expert witnesses on specific causation. Given that about 75% of AML cases are idiopathic, the court held that the plaintiffs’ expert witnesses attempt to proffer a differential etiology was fatally flawed.[16]

The authors cite the Milward specific causation decision, which in turn channeled the Restatement (Third) by couching the argument in terms of probability. If the claimant is left with a disjunction, [tortogen OR idiopathogen], then they suggest a probability value be assigned to the idiopathogen to support the inference that the probability that the tortogen was responsible for the claimant’s outcome [(1 – P(idiopathogen) x 100%]. Or in Judge Woodlock’s words:

“When a disease has a discrete set of causes, eliminating some number of them significantly raises the probability that the remaining option or options were the cause-in-fact of the disease. Restatement (Third) of Torts: Phys. & Emot. Harm § 28, cmt. c (2010) (‘The underlying premise [of differential etiology] is that each of the [ ] known causes is independently responsible for some proportion of the disease in a given population. Eliminating one or more of these as a possible cause for a specific plaintiff’s disease increases the probability that the agent in question was responsible for that plaintiff’s disease.’). The same cannot be said when eliminating a few possible causes leaves not only fewer possible causes but also a high probability that a cause cannot be identified. (‘When the causes of a disease are largely unknown . . . differential etiology is of little assistance.’).”[17]

The Milward approach is thus a vague, indirect invocation of relative risks and attributable fractions, without specifying the probabilities involved in quantitative terms.  Like obscenity, judges are supposed to discern when the residual probability of idiopathy is too great to permit an inference of specific causation. Somehow, I have the sense we should be able to do better than this.

Multiple Risks

To their credit, the authors tackle the difficult cases that arise when multiple risks are present. Those multiple risks may be competing risks, including the tortogen, in which case not all participate in bringing about the outcome. Indeed, if there is a baseline risk, the result may still have come about from an idiopathogen. The discussion in Differential Etiologies take some twists and turns, and I will not discuss all of it here.

Strong tortogen versus one weak competing risk

The authors describe the scenario of strong tortogen versus a single competing risk as one of the “easy cases,” at least when the alternative cause appears to be de minimus:

“If the choice of whether one’s lung cancer was the result of a lifetime of heavy smoking or by a brief encounter with a substance for which there is a significant but weak correlation with lung cancer, in most situations it should be an easy task to rule out the other substance as the specific cause of the individual’s injury.”[18]

Unfortunately, the article’s discussion leaves everything rather vague, without quantifying the risks involved. We can, without too much effort, provide some numbers, although we cannot be sure that the authors would accept the resulting quantification. If the claimant’s lifetime of heavy smoking carried a relative risk of 30, and the claimant worked for a few years in a truck depot where he was exposed to diesel fumes that carried a relative risk of 1.2, it would seem that it should be “an easy task” to rule out diesel fumes and rule in smoking. Note however that ease of the inference is lubricated by the size of the relative risks involved, one much larger than two, and the other much smaller than two, and the absence of any suggestion of interaction or synergy between them. If the tortogen in this scenario is tobacco, the plaintiff wins readily. If the tortogen is diesel fumes, the plaintiff loses. Query, if this scenario arises in a case against the tobacco company, whether the alternative causation defense of exposure to diesel fumes fails as a matter of law?

Synergy between strong tortogen and strong competing risk

The authors cannot resist the temptation to cite the Mt. Sinai catechism[19] of multiplicative risk from smoking and asbestos exposure[20]:

“A well-known example of a synergistic effect is the combined effect of asbestos exposure and smoking on the likelihood of developing lung cancer. For long-term smokers, the relative risk of developing lung cancer compared to those who have never smoked is sometimes estimated to be in the range of 10.0. For individuals substantially exposed to asbestos, the relative risk of developing lung cancer compared to non-exposed individuals is in the range of 5.0. However, if one is unfortunate enough to have been exposed to asbestos and to have been a long-term smoker, the relative risk compared to those unexposed individuals who have not smoked exceeds the sum of the relative risks. One possibility is that the relationship is multiplicative, in the range of 50.0—i.e., a 49-fold risk increment.”[21]

The synergistic interaction is often raised in an attempt to defeat causal apportionment or avoid responsibility for the larger risk, as when smokers attempt to recover for lung cancer from asbestos exposure. Some courts have, however, permitted causal apportionment. In their analysis, the authors of Differential Etiologies simply wink and tell us that “[t]he calculation of synergistic effects is fairly complex.”[22]

Tortogen versus Multiple Risks

The scenario in which the tortogen has been “ruled in,” and is present in the claimant’s history, along with multiple other risks is more difficult than one might have imagined. The authors tell us that an individual claimant will fail to show that the tortogen is more likely than not a cause of her injury when one or more of the competing risks is stronger than the risk from the tortogen (assuming no synergy).[23] The authors’ analysis leaves unclear why the claimant does not similarly fail when the strength of the tortogen is equal to that of a competing risk. Similarly, the claimant would appear to have fallen short of the burden of proving the tortogen’s causal role when there are multiple competing risk factors that individually present smaller risks than the tortogen, but for which multiple subsets represent combined competing risks greater than the risk of the tortogen. 

Concluding Thoughts

If the authors had framed the differential enterprise by the logic of iterative disjunctive syllogism, they would have recognized that the premise of the argument must contain the disjunction of all general causes that might have been a cause of the claimant’s disease or injury. Furthermore, unless the idiopathogen(s) is eliminated, which rarely is the case, we are left with a disjunction in the conclusion that prevents judgment for the plaintiff. The extensive analysis provided in Differential Etiologies ultimately must equate risk with cause, and it must do so on a probabilistic basis, even when the probabilities are left vague, and unquantified. Indeed, the authors come close to confronting the reality that we often do not know the cause of many individual’s diseases. We do know something about the person’s antecedent risks, and we can quantify and compare those risks. Noncommittally, the authors note that courts have been receptive to the practical solution of judging whether the tortogen’s relative risk was greater than two as a measure of sufficiency for specific causation, and that they “agree that theoretically this intuition has appeal.”[24]

Although I have criticized many aspect of the article, it is an important contribution to the legal study of specific causation. Its taxonomy will not likely be the final word on the subject, but it is a major step toward making sense of an area of the law long dominated by clinical black boxes and ipse dixits.


[1] Differential Etiologies at 885. The authors noted that their advice was “especially true in those case-control studies where the cases and controls are not drawn from the same defined population at risk for the outcome under investigation.”

[2] Differential Etiologies at 895.

[3] Differential Etiologies at 895 & n. 154, citing Betz v. Pneumo Abex, LLC, 44 A.3d 27, 51 (Pa. 2012).

[4] Differential Etiologies at 895 at n. 156.

[5] American Cancer Soc’y website, last visited June 19, 2022.

[6] I did not know at the time that Selikoff had failed the B-reader examination.

[7] See, e.g., Bowers v. Norfolk Southern Corp., 537 F. Supp. 2d 1343, 1359–60 (M.D. Ga. 2007) (“The differential diagnosis method has an inherent reliability; the differential etiology method does not. This conclusion does not suggest that the differential etiology approach has no merit. It simply means that courts, when dealing with matters of reliability, should consider opinions based on the differential etiology method with more caution. It also means that courts should not conflate the two definitions.”)

[8] Differential Etiologies at 899 & n.176, citing Landrigan v. Celotex Corp., 127 N.J. 404, 605 A.2d 1079, 1087 (1992).

[9] Differential Etiologies at 899 & n.179, citing Johnson & Johnson Talcum Powder Cases, 249 Cal. Rptr. 3d 642, 671–72 (Cal. Ct. App. 2019).

[10] Caterinicchio v. Pittsburgh Corning Corp., 127 N.J. 428, 605 A.2d 1092 (1992).

[11] Differential Etiologies at 899.

[12] Differential Etiologies at 900.

[13] Differential Etiologies at 915.

[14] 639 F.3d 11 (1st Cir. 2011).

[15] Does it require pointing out that the reversal took place with a highly questionable, unethical amicus brief submitted by a not-for-profit that was founded by the two plaintiffs’ expert witnesses excluded by the trial court? Given that the First Circuit reversed and remanded, and then later affirmed the exclusion of plaintiffs’ expert witnesses on specific causation, and the entry of judgment, the first appellate decision became unnecessary to the final judgment and no longer a clear precedent.

[16] Differential Etiologies at 912, discussing Milward v. Acuity Specialty Prods. Group, Inc., 969 F. Supp. 2d 101, 109 (D. Mass. 2013), aff’d sub. nom., Milward v. Rust-Oleum Corp., 820 F.3d 469, 471, 477 (1st Cir. 2016).

[17] Id., quoting from Milward.

[18] Differential Etiologies at 901.

[19]  “The Mt. Sinai Catechism” (June 11, 2013).

[20] The mantra of 5-10-50 comes from early publications by Irving John Selikoff, and represents a misrepresentation of “never smoked regularly” as “never smoked,” and the use of a non-contemporaneous control group for the non-asbestos exposed, non-smoker base rate. When the external control group was updated to show a relative risk of 20, rather than 10 for smoking only, Selikoff failed to update his analysis. Selikoff’s protégés have recently updated the insulator cohort, repeating many of the original errors, but even so, finding only that “the joint effect of smoking and asbestos alone was additive.” See Steve Markowitz, Stephen Levin, Albert Miller, and Alfredo Morabia, “Asbestos, Asbestosis, Smoking and Lung Cancer: New Findings from the North American Insulator Cohort,” Am. J. Respir. & Critical Care Med. (2013).

[21] Differential Etiologies at 902. The authors do not cite the Selikoff publications, which repeated his dataset and his dubious interpretation endlessly, but rather cite David Faigman, et al., Modern Scientific Evidence: The Law and Science of Expert Testimony § 26.25. (West 2019–2020 ed.). To their credit, the authors describe multiplicative interaction as a possibility, but surely they known that plaintiffs’ expert witnesses recite the Mt. Sinai catechism in courtrooms all around the country, while intoning “reasonable degree of medical certainty.” The authors cite some contrary studies. Differential Etiologies at 902 n.188, citing several reviews including Darren Wraith & Kerrie Mengersen, “Assessing the Combined Effect of Asbestos Exposure & Smoking on Lung Cancer: A Bayesian Approach, 26 Stats. Med. 1150, 1150 (2007) (evidence supports more than an additive model and less than a multiplicative relation).”

[22] Differential Etiologies at 902 at n.189.

[23] Differential Etiologies at 905. The authors note that courts have admitted differential etiology testimony when the tortogen’s risk is greater than the risk from other known risks. Id. citing Cooper v. Takeda Pharms., 191 Cal. Rptr. 3d 67, 79 (Ct. App. 2015).

[24] Differential Etiologies at 896 & n.163.

Differential Etiologies – Part One – Ruling In

June 17th, 2022

You put your right foot in

You put your right foot out

You put your right foot in

And you shake it all about

You do the Hokey Pokey and you turn yourself around

That’s what it’s all about!

 

Ever since the United States Supreme Court decided Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993), legal scholars, judges, and lawyers have struggled with the structure and validity of expert opinion on specific causation. Professor David Faigman and others have attempted to articulate the scientific basis (if any) for opinion testimony in health-effects litigation that a give person’s disease has been caused by an exposure or condition.

In 2015, as part of a tribute to the late Judge Jack Weinstein, Professor Faigman offered the remarkable suggestion that in advancing differential etiologies, expert witnesses were inventing wholesale an approach that had no foundation or acceptance in their scientific disciplines:

 “Differential etiology is ostensibly a scientific methodology, but one not developed by, or even recognized by, physicians or scientists. As described, it is entirely logical, but has no scientific methods or principles underlying it. It is a legal invention and, as such, has analytical heft, but it is entirely bereft of empirical grounding. Courts and commentators have so far merely described the logic of differential etiology; they have yet to define what that methodology is.”[1]

Faigman is correct that courts often have left unarticulated exactly what the methodology is, but he does not quite make sense when he writes that the method of differential etiology is “entirely logical,” but has no “scientific methods or principles underlying it.” After all, Faigman starts off his essay with a quotation from Thomas Huxley that “science is nothing but trained and organized common sense.”[2] As I have written elsewhere, the form of reasoning involved in differential diagnosis is nothing other than iterative disjunctive syllogism.[3] Either-or reasoning occurs throughout the physical and biological sciences; it is not clear why Faigman declares it un- or extra-scientific.

The strength of Faigman’s claim about the made-up nature of differential etiology appears to be undermined and contradicted by an example that he provides from clinical allergy and immunology:

“Allergists, for example, attempt to identify the etiology of allergic reactions in order to treat them (or to advise the patient to avoid what caused them), though it might still be possible to treat the allergic reactions without knowing their etiology.”

Faigman at 437. Of course, not only allergists try to determine the cause of an individual patient’s disease. Psychiatrists, in the psychoanalytic tradition, certain do so as well. Physicians who use predictive regression models use group data, in multivariate analyses, to predict outcomes, risk, and mortality in individual patients. Faigman’s claim is similarly undermined by the existence of a few diseases (other than infectious diseases) that are defined by the causative exposure. Silicosis and manganism have played a large role in often bogus litigation, but they represent instances in which a differential diagnosis and puzzle may also be an etiological diagnosis and a puzzle. Of course, to the extent that a disease is defined in terms of causative exposures, there may be serious and even intractable problems caused by the lack of specificity and accuracy in the diagnostic criteria for the supposedly pathognomonic disease.

As I noted at the time of Faigman’s 2015 essay, his suggestion that the concept of “differential etiology” was not used in the sciences themselves, was demonstrably flawed and historically inaccurate.[4]

A year earlier, in a more sustained analysis of specific causation, Professor Faigman went astray in a different direction, this time by stating that:

“it is not customary in the ordinary practice of sociology, epidemiology, anthropology, and related fields (for example, cognitive and social psychology) for professionals to make individual diagnostic judgments derived from group-based data.”[5]

Faigman’s invocation of “ordinary practice” of epidemiology was seriously wide of the mark. Medical practitioners and scientists frequently use epidemiologic data, based upon “group-based data” to make individual diagnostic judgments. The inferences from group data to individual range abound in the diagnostic process itself, where the specificity and sensitivity of disease signs and symptoms are measured by group data. Physicians must rely upon group data to make prognoses for individual patients, and they rely upon group data to predict future disease risks for individual patients. Future disease risks, as in the Framingham risk score for hard coronary heart disease, or the Gale model for breast cancer risk, are, of course, based upon “group-based data.” Medical decisions to intervene, surgically, pharmacologically, or by some other method, all involve applying group data to the individual patient.

Faigman’s 2014 law review article was certainly correct, however, in noting that specific causation inferences and conclusions were often left “profoundly underdefined,” with glib identifications of risk with cause.[6] There was thus plenty of room for further elucidation of specific causation decisions, and I welcome Faigman’s most recent effort to nail conceptual jello to the wall, in a law review article that was published last year.[7]

This new article, “Differential Etiology: Inferring Specific Causation in the Law from Group Data in Science,” is the collaborative product of Professor Faigman and three other academics. Joseph Sanders will be immediately recognizable to the legal community as someone who long pondered causation issues, both general and specific, and who has contributed greatly to the law review literature on causation of health outcomes. In addition to the law professors, Peter B. Imrey, a professor of medicine at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, and Philip Dawid, an emeritus professor of statistics in Cambridge University, have joined the effort to make sense of specific causation in the law. The addition of medical and statistical expertise has added greatly to Faigman’s previous efforts, and it has corrected some of his earlier errors and added much nuance to the discussion. The resulting law review article is well worth reading for practitioners. In this post, however, I have not detailed every important insight, but rather I have tried to point out some of the continuing and new errors in the analysis.

The Sanders-Faigman-Imbrey-Dawid analysis begins with a lament that:

“there is no body of science to which experts can turn when addressing this issue. Ultimately, much of the evidence that can be brought to bear on this causal question is the same group-level data employed to prove general causation. Consequently, the expert testimony often feels jerry-rigged, an improvisation designed to get through a tough patch.”[8]

As an assessment of the judicial decisions on specific causation, there can be no dissent or appeal from the judgment of these authors. The authors use of the term “jerry-rigged” is curious. I had first I thought they were straining to avoid using the common phrase “jury rigged” or to avoid inventing a neologism such as “judge rigged.” The American Heritage and Merriam Webster dictionaries, however, describe the phrase “jerry-rigged” as a conflation of “jury-rigged,” a nautical term for a temporary but adequate repair, with “jerry-rigged,” a war-time pejorative term for makeshift devices put together by Germans. So jerry-rigged it is, and the authors are off and running to try to describe, clarify, and justify the process of drawing specific causation inferences by differential etiology. They might have called what passes for judicial decision making in this area as the “hokey pokey.”

The authors begin their analysis of specific causation with a brief acknowledgement that our legal system could abandon any effort to set standards or require rigorous thinking on the matter by simply leaving the matter to the jury.[9] After all, this laissez-faire approach had been the rule of law for centuries. Nevertheless, despite occasional retrograde, recidivist judicial opinions,[10] the authors realize that the law has evolved to a point that some judicial control over specific causation opinions is required. And if judges are going to engage in gatekeeping of specific-causation opinions, they need to explain and justify their decisions in a coherent and cogent fashion.

Having thus dispatched legal nihilism, the authors turn their attention to what they boldly describe as “the first full-scale effort to bring scientific sensibilities – and rigorous statistical thinking – to the legally imperative concept of specific causation.”[11] The claim is remarkable claim given that tort law has been dealing with the issue for decades, but probably correct given how frequently judges have swept the issue under a judicial rug of inpenetrable verbiage and shaggy thinking. The authors also walk back some of Faigman’s earlier claims that there is no science in the assessment of specific causation, although they acknowledge the obvious, that policy issues sometimes play a role in deciding both general and specific causation decisions. The authors also offer the insight, for which they claim novelty, that some of the Bradford Hill guidelines, although stated as part of assessing general causation, have some relevancy to decisions concerning specific causation.[12] Their insight is indeed important, although hardly novel.

Drawing upon some of the clearer judicial decisions, the authors identify three necessary steps to reach a conclusion of specific causation:

“(a) making a proper diagnosis;

(b) supporting (“ruling in”) the plausibility of the alleged cause of the injury on the basis of general evidence and logic; and

(c) particularization, i.e., excluding (‘ruling out’) competing causes in the specific instance under consideration.”[13]

Although this article is ostensibly about specific causation, the authors do not reach a serious discussion of the matter until roughly the 42nd page of a 72 page article. Having described a three-step approach, the authors feel compelled to discuss step one (describing or defining the “diagnosis,” or the outcome of interest), and step two, the “ruling in” process that requires an assessment of general causation.

Although ascertaining general causation is not the focus of this article, the authors give an extensive discourse on it. Indeed, the authors have some useful things to say about steps one and two, and I commend the article to readers for some of its learning. As much as the lawsuit industry might wish to do away with the general causation step, it is not going anywhere soon.[14] The authors also manage to say some things that range from wrong to not even wrong. One example of professoriate wish casting is the following assertion:

“Other things being equal, when the evidence for general causation is strong, and especially when the strength of the exposure–disease relationship as demonstrated in a body of research is substantial, the plaintiff faces a lower threshold in establishing the substance as the cause in a particular case than when the relationship is weaker.”[15]

This assertion appears, sans citation or analysis. The generalization fails in the face of counterexamples. The causal role for estrogen in many breast cancers is extremely strong. The International Agency for Cancer Research classifies estrogen as a Category I, known human carcinogen for breast cancer, even though estrogen is made naturally in the human female, and male, body. In the Women’s Health Initiative clinical trial, researchers reported a hazard ratio of 1.2,[16] but plaintiffs struggled to prevail on specific causation in litigation involving claims of breast cancer caused by post-menopausal hormone therapy. Perhaps the authors meant, by strength of exposure relationship, a high relative risk as well, but that point is taken up when the authors address the “ruling in” step of the three-step approach. In any event, the strength of the case for general causation is quite independent of the specific causation inference, especially in the face of small effect sizes.

On general causation itself, the authors begin their discussion with “threats to validity,” a topic that they characterize as mostly implicit in the Bradford Hill guidelines. But their suggestion that validity is merely implicit in the guidelines is belied by their citation to Dr. Woodside’s helpful article on the “forgotten predicate” to the nine Bradford Hill guidelines.[17] Bradford Hill explicitly noted that the starting point for considering an association to be causal occurred when “[o]ur observations reveal an association between two variables, perfectly clear-cut and beyond what we would care to attribute to the play of chance.”[18] Sir Austin told us in no uncertain terms that there is no need to consider the nine guidelines until random and systematic error have been rejected.[19]

In this article’s discussion of general causation, Professor’s Dawid’s influence can be seen in the unusual care to describe and define the p-value.[20] But the discussion devolves into more wish casting, when the authors state that p-values are not the only way to assess random error in research results.

They double down by stating that “[m]any prominent statisticians and other scientists have questioned it, and the need for change is increasingly accepted.”[21] The source for their statement, the American Statistical Association (ASA) 2016 p-value Statement, did not questioned the utility of the p-value for assessing random error, and this law review provides no other support for other unidentified methods to assess random error. For the most part, the ASA Statement identified misuses and misstatements of p-values, with the caveat that “[s]cientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.” This is hardly questioning the importance or utility of p-values in assessing random error.

When one of the cited authors, Ronald Wasserstein, published an editorial in 2019, proclaiming that it was time to move past the p-value, the then president of the ASA, Professor Karen Kafadar, commissioned a task force on the matter. That task force, consisting of many of the world’s leading statisticians, issued a short, but pointed rejection of Wasserstein’s advocacy, and by implication, the position asserted in this law review.[22] Several of the leading biomedical journals that were lobbied by Wasserstein to abandon statistical significance testing reassessed their statistical guidelines and reaffirmed the use of p-values and tests.[23]

Similarly, this law review’s statements that alternatives to frequentist tests (p-values) such as Bayesian inference are “ascendant” have no supporting citations, and generally are an inaccurate assessment of what most biomedical journals are currently publishing.

Despite the care with which this law review article has defined p-values, the authors run off the road when defining a confidence interval:

A 95% confidence interval … is a one-sided or two-sided interval from a data sample with 95% probability of bounding a fixed, unknown parameter, for which no nondegenerate probability distribution is conceived, under specified assumptions about the data distribution.”[24]

The emphasis added is to point out that the authors assigned a single confidence interval with the property of bounding the true parameter with 95% probability. That property, however, belongs to the infinite set of confidence intervals based upon repeated sampling of the same size from the same population, and constant variance. There is no probability statement to be made for the true parameter, as either in or not in a given confidence interval.

In an issue that is relevant to general and specific causation, the authors offer some ipse dixit on the issue of “thresholds”:

“with respect to some substance/injury relationships, it is thought that there is no safe threshold. Cancer is the injury for which it is most frequently thought that there is no safe threshold, but even here the mechanism of injury may lead to a different conclusion.”[25]

Here as elsewhere, the authors are repeating dogma, not science, and they ignore the substantial body of scientific evidence that undermines the so-called linear no threshold dose-response curve. The only citation offered is a judicial citation to a case that rejected the no threshold position![26]

So much for “ruling in.” In the next post, I will turn my attention to this law review’s handling of the “ruling out” step of differential etiology.


[1] David L. Faigman & Claire Lesikar, “Organized Common Sense: Some Lessons from Judge Jack Weinstein’s Uncommonly Sensible Approach to Expert Evidence,” 64 DePaul L. Rev. 421, 444 (2015).

[2] Thomas H. Huxley, “On the Education Value of the Natural History Sciences” (1854), in Lay Sermons, Addresses and Reviews 77 (1915).

[3] See, e.g., “Differential Etiology and Other Courtroom Magic” (June 23, 2014) (collecting cases); “Differential Diagnosis in Milward v. Acuity Specialty Products Group” (Sept. 26, 2013).

[4] See David Faigman’s Critique of G2i Inferences at Weinstein Symposium (Sept. 11, 2015); Kløve & D. Doehring, “MMPI in epileptic groups with differential etiology,” 18 J. Clin. Psychol. 149 (1962); Kløve & C. Matthews, “Psychometric and adaptive abilities in epilepsy with differential etiology,” 7 Epilepsia 330 (1966); Teuber & K. Usadel, “Immunosuppression in juvenile diabetes mellitus? Critical viewpoint on the treatment with cyclosporin A with consideration of the differential etiology,” 103 Fortschr. Med. 707 (1985); G.May & W. May, “Detection of serum IgA antibodies to varicella zoster virus (VZV)–differential etiology of peripheral facial paralysis. A case report,” 74 Laryngorhinootologie 553 (1995); Alan Roberts, “Psychiatric Comorbidity in White and African-American Illicity Substance Abusers” Evidence for Differential Etiology,” 20 Clinical Psych. Rev. 667 (2000); Mark E. Mullinsa, Michael H. Leva, Dawid Schellingerhout, Gilberto Gonzalez, and Pamela W. Schaefera, “Intracranial Hemorrhage Complicating Acute Stroke: How Common Is Hemorrhagic Stroke on Initial Head CT Scan and How Often Is Initial Clinical Diagnosis of Acute Stroke Eventually Confirmed?” 26 Am. J. Neuroradiology 2207 (2005);Qiang Fua, et al., “Differential Etiology of Posttraumatic Stress Disorder with Conduct Disorder and Major Depression in Male Veterans,” 62 Biological Psychiatry 1088 (2007); Jesse L. Hawke, et al., “Etiology of reading difficulties as a function of gender and severity,” 20 Reading and Writing 13 (2007); Mastrangelo, “A rare occupation causing mesothelioma: mechanisms and differential etiology,” 105 Med. Lav. 337 (2014).

[5] David L. Faigman, John Monahan & Christopher Slobogin, “Group to Individual (G2i) Inference in Scientific Expert Testimony,” 81 Univ. Chi. L. Rev. 417, 465 (2014).

[6] Id. at 448.

[7] Joseph Sanders, David L. Faigman, Peter B. Imrey, and Philip Dawid, “Differential Etiology: Inferring Specific Causation in the Law from Group Data in Science,” 63 Ariz. L. Rev. 851 (2021) [Differential Etiology]. I am indebted to Kirk Hartley for calling this new publication to my attention.

[8] Id. at 851, 855.

[9] Id. at 855 & n. 8 (citing A. Philip Dawid, David L. Faigman & Stephen E. Fienberg, “Fitting Science into Legal Contexts: Assessing Effects of Causes or Causes of Effects?,” 43 Sociological Methods & Research 359, 363–64 (2014). See also Barbara Pfeffer Billauer, “The Causal Conundrum: Examining the Medical-Legal Disconnect in Toxic Tort Cases from a Cultural Perspective or How the Law Swallowed the Epidemiologist and Grew Long Legs and a Tail,” 51 Creighton L. Rev. 319 (2018) (arguing for a standard-less approach that allows clinicians to offer their ipse dixit opinions on specific causation).

[10] Differential Etiology at 915 & n.231, 919 & n.244 (citing In re Round-Up Prods. Liab. Litig., 358 F. Supp. 3d 956, 960 (N.D. Cal. 2019).

[11] Differential Etiology at 856 (emphasis added).

[12] Differential Etiology at 857.

[13] Differential Etiology at 857 & n.14 (citing Best v. Lowe’s Home Ctrs., Inc., 563 F.3d 171, 180 (6th Cir. 2009)).

[14] See Margaret Berger, “Eliminating General Causation: Notes Toward a New Theory of Justice and Toxic Torts,” 97 Colum L. Rev. 2117 (1997).

[15] Differential Etiology at 864.

[16] Jacques E. Rossouw, et al.,Risks and benefits of estrogen plus progestin in healthy postmenopausal women: Principal results from the Women’s Health Initiative randomized controlled trial,” 288 J. Am. Med. Ass’n 321 (2002).

[17] Differential Etiology at 884 & n.104, citing Frank Woodside & Allison Davis, “The Bradford Hill Criteria: The Forgotten Predicate,” 35 Thomas Jefferson L. Rev. 103 (2013).

[18] Austin Bradford Hill, “The Environment and Disease: Association or Causation?” 58 Proc. Royal Soc’y Med. 295, 295 (1965).  

[19] Differential Etiology at 865.

[20] Differential Etiology at 869.

[21] Differential Etiology at 872, citing Ronald L. Wasserstein and Nicole A. Lazar, “The ASA Statement on p-Values: Context, Process, and Purpose,” 72 Am. Statistician 129 (2016).

[22] Yoav Benjamini, Richard D. De Veaux, Bradley Efron, Scott Evans, Mark Glickman, Barry I. Graubard, Xuming He, Xiao-Li Meng, Nancy M. Reid, Stephen M. Stigler, Stephen B. Vardeman, Christopher K. Wikle, Tommy Wright, Linda J. Young, and Karen Kafadar, “ASA President’s Task Force Statement on Statistical Significance and Replicability,” 15 Ann. Applied Statistics 1084 (2021), 34 Chance 10 (2021).

[23] See “Statistical Significance at the New England Journal of Medicine” (July 19, 2019); See also Deborah G. Mayo, “The NEJM Issues New Guidelines on Statistical Reporting: Is the ASA P-Value Project Backfiring?” Error Statistics Philosophy  (July 19, 2019).

[24] Differential Etiology at 898 n.173 (emphasis added).

[25] Differential Etiology at 890.

[26] Differential Etiology at n.134, citing Chlorine Chemistry Council v. Envt’l Protection Agency, 206 F.3d 1286 (D.C. Cir. 2000), which rejected the agency’s assumption that the carcinogenic effects of chloroform in drinking water lacked a threshold.

Improper Reliance upon Regulatory Risk Assessments in Civil Litigation

March 19th, 2022

Risk assessments would seemingly be about assessing risks, but they are not. The Reference Manual on Scientific Evidence defines “risk” as “[a] probability that an event will occur (e.g., that an individual will become ill or die within a stated period of time or by a certain age).”[1] The risk in risk assessment, however, may be zero, or uncertain, or even a probability of benefit. Agencies that must assess risks and set “action levels,” or “permissible exposure limits,” or “acceptable intakes,” often work under great uncertainty, with inspired guesswork, using unproven assumptions.

The lawsuit industry has thus often embraced the false equivalence between agency pronouncements on harmful medicinal, environmental, or occupational exposures and civil litigation adjudication of tortious harms. In the United States, federal agencies such as the Occupational Safety and Health Administration (OSHA), or the Environmental Protection Agency (EPA), and their state analogues, regularly set exposure standards that could not and should not hold up in a common-law tort case. 

Remarkably, there are state and federal court judges who continue to misunderstand and misinterpret regulatory risk assessments, notwithstanding efforts to educate the judiciary. The second edition of the Reference Manual on Scientific Evidence contained a chapter by the late Professor Margaret Berger, who took pains to point out the difference between agency assessments and the adjudication of causal claims in court:

[p]roof of risk and proof of causation entail somewhat different questions because risk assessment frequently calls for a cost-benefit analysis. The agency assessing risk may decide to bar a substance or product if the potential benefits are outweighed by the possibility of risks that are largely unquantifiable because of presently unknown contingencies. Consequently, risk assessors may pay heed to any evidence that points to a need for caution, rather than assess the likelihood that a causal relationship in a specific case is more likely than not.[2]

In March 2003, Professor Berger organized a symposium,[3] the first Science for Judges program (and the last), where the toxicologist Dr. David L. Eaton presented on the differences in the use of toxicology in regulatory pronouncements as opposed to causal assessments in civil actions. As Dr. Eaton noted:

“regulatory levels are of substantial value to public health agencies charged with ensuring the protection of the public health, but are of limited value in judging whether a particular exposure was a substantial contributing factor to a particular individual’s disease or illness.”[4]

The United States Environmental Protection Agency (EPA) acknowledges that estimating “risk” from low level exposures based upon laboratory animal data is fraught because of inter-specie differences in longevity, body habitus and size, genetics, metabolism, excretion patterns, genetic homogeneity of laboratory animals, dosing levels and regimens. The EPA’s assumptions in conducting and promulgating regulatory risk assessments are intended to predict the upper bound of theoretical risk, while fully acknowledging that there may be no actual risk in humans:

“It should be emphasized that the linearized multistage [risk assessment] procedure leads to a plausible upper limit to the risk that is consistent with some proposed mechanisms of carcinogenesis. Such an estimate, however, does not necessarily give a realistic prediction of the risk. The true value of the risk is unknown, and may be as low as zero.”[5]

The approach of the U.S. Food and Drug Administration (FDA) with respect to mutagenic impurities in medications provides an illustrative example of how theoretical and hypothetical risk assessment can be.[6] The FDA’s risk assessment approach is set out in a “Guidance” document, which like all such FDA guidances, describes itself as containing non-binding recommendations, which do not preempt alternative approaches.[7] The agency’s goal is devise a control strategy for any mutagenic impurity to keep it at or below an “acceptable cancer risk level,” even if the risk or the risk level is completely hypothetical.

The FDA guidance advances the concept of a “Threshold of Toxicological Concern (TTC),” to set an “acceptable intake,” for chemical impurities that pose negligible risks of toxicity or carcinogenicity.[8] The agency describes its risk assessment methodology as “very conservative,” given the frequently unproven assumptions made to reach a quantification of an “acceptable intake”:

“The methods upon which the TTC is based are generally considered to be very conservative since they involve a simple linear extrapolation from the dose giving a 50% tumor incidence (TD50) to a 1 in 10-6 incidence, using TD50 data for the most sensitive species and most sensitive site of tumor induction. For application of a TTC in the assessment of acceptable limits of mutagenic impurities in drug substances and drug products, a value of 1.5 micrograms (µg)/day corresponding to a theoretical 10-5 excess lifetime risk of cancer can be justified.”

For more potent mutagenic carcinogens, such as aflatoxin-like-, N-nitroso-, and alkyl-azoxy compounds, the acceptable intake or permissible daily exposure (PDE) is set lower, based upon available animal toxicologic data.

The important divide between regulatory practice and the litigation of causal claims in civil actions arises from the theoretical nature of the risk assessment enterprise. The FDA acknowledges, for instance, that the acceptable intake is set to mark “a small theoretical increase in risk,” and a “highly hypothetical concept that should not be regarded as a realistic indication of the actual risk,” and thus not an actual risk.[9] The corresponding hypothetical or theoretical risk to the acceptable intake level is clearly small when compared with the human’s lifetime probability of developing cancer (which the FDA states is greater than 1/3, but probably now approaches 40%).

Although the TTC concept allows a calculation of an estimated “safe exposure,” the FDA points out that:

“exceeding the TTC is not necessarily associated with an increased cancer risk given the conservative assumptions employed in the derivation of the TTC value. The most likely increase in cancer incidence is actually much less than 1 in 100,000. *** Based on all the above considerations, any exposure to an impurity that is later identified as a mutagen is not necessarily associated with an increased cancer risk for patients already exposed to the impurity. A risk assessment would determine whether any further actions would be taken.”

In other words the FDA’s risk assessment exists to guide agency action, not to determine a person’s risk or medical status.[10]

As small and theoretical as the risks are, they are frequently based upon demonstrably incorrect assumptions, such as:

  1. humans are as sensitive as the most sensitive species;
  2. all organs are as sensitive as the most sensitive organ of the most sensitive species;
  3. the dose-response in the most sensitive species is a simple linear relationship;
  4. the linear relationship runs from zero exposure and zero risk to the exposure that yields the so-called TD50, the exposure that yields tumors in 50% of the experimental animal model;
  5. the TD-50 is calculated based upon the point estimate in the animal model study, regardless of any confidence interval around the point estimate;
  6. the inclusion, in many instances, of non-malignant tumors as part of the assessment of the TD50 exposure;
  7. there is some increased risk for any exposure, no matter how small; that is, there is no threshold below which there is no increased risk; and
  8. the medication with the mutagenic impurity was used daily for 70 years, by a person who weights 50 kg.

Although the FDA acknowledges that there may be some instances in which a “less than lifetime level” (LTL) may be appropriate, it places the burden on manufacturers to show the appropriateness of higher LTLs. The FDA’s M7 Guidance observes that

“[s]tandard risk assessments of known carcinogens assume that cancer risk increases as a function of cumulative dose. Thus, cancer risk of a continuous low dose over a lifetime would be equivalent to the cancer risk associated with an identical cumulative exposure averaged over a shorter duration.”[11]

Similarly, the agency acknowledges that there may be a “practical threshold,” as result of bodily defense mechanisms, such as DNA repair, which counter any ill effects from lower level exposures.[12]

“The existence of mechanisms leading to a dose response that is non-linear or has a practical threshold is increasingly recognized, not only for compounds that interact with non-DNA targets but also for DNA-reactive compounds, whose effects may be modulated by, for example, rapid detoxification before coming into contact with DNA, or by effective repair of induced damage. The regulatory approach to such compounds can be based on the identification of a No-Observed Effect Level (NOEL) and use of uncertainty factors (see ICH Q3C(R5), Ref. 7) to calculate a permissible daily exposure (PDE) when data are available.”

Expert witnesses often attempt to bootstrap their causation opinions by reference to determinations of regulatory agencies that are couched in similar language, but which use different quality and quantity of evidence than is required in the scientific community or in civil courts.

Supreme Court

Industrial Union Dep’t v. American Petroleum Inst., 448 U.S. 607, 656 (1980) (“OSHA is not required to support its finding that a significant risk exists with anything approaching scientific certainty” and “is free to use conservative assumptions in interpreting the data with respect to carcinogens, risking error on the side of overprotection, rather than underprotection.”).

Matrixx Initiatives, Inc. v. Siracusano, 563 U.S. 27, 131 S.Ct. 1309, 1320 (2011) (regulatory agency often makes regulatory decisions based upon evidence that gives rise only to a suspicion of causation) 

First Circuit

Sutera v. Perrier Group of America, Inc., 986 F. Supp. 655, 664-65, 667 (D. Mass. 1997) (a regulatory agency’s “threshold of proof is reasonably lower than that in tort law”; “substances are regulated because of what they might do at given levels, not because of what they will do. . . . The fact of regulation does not imply scientific certainty. It may suggest a decision to err on the side of safety as a matter of regulatory policy rather than the existence of scientific fact or knowledge. . . . The mere fact that substances to which [plaintiff] was exposed may be listed as carcinogenic does not provide reliable evidence that they are capable of causing brain cancer, generally or specifically, in [plaintiff’s] case.”); id. at 660 (warning against the danger that a jury will “blindly accept an expert’s opinion that conforms with their underlying fears of toxic substances without carefully understanding or examining the basis for that opinion.”). Sutera is an important precedent, which involved a claim that exposure to an IARC category I carcinogen, benzene, caused plaintiffs’ leukemia. The plaintiff’s expert witness, Robert Jacobson, espousing a “linear, no threshold” theory, and relying upon an EPA regulation, which he claimed supported his opinion that even trace amounts of benzene can cause leukemia.

In re Neurontin Mktg., Sales Practices, and Prod. Liab. Litig., 612 F. Supp. 2d 116, 136 (D. Mass. 2009) (‘‘It is widely recognized that, when evaluating pharmaceutical drugs, the FDA often uses a different standard than a court does to evaluate evidence of causation in a products liability action. Entrusted with the responsibility of protecting the public from dangerous drugs, the FDA regularly relies on a risk-utility analysis, balancing the possible harm against the beneficial uses of a drug. Understandably, the agency may choose to ‘err on the side of caution,’ … and take regulatory action such as revising a product label or removing a drug from the marketplace ‘upon a lesser showing of harm to the public than the preponderance-of-the-evidence or more-like-than-not standard used to assess tort liability’.’’) (internal citations omitted) 

Whiting v. Boston Edison Co., 891 F. Supp. 12, 23-24 (D. Mass. 1995) (criticizing the linear no-threshold hypothesis, common to regulatory risk assessments, because it lacks any known or potential error rate, and it cannot be falsified as would any scientific theory)

Second Circuit

Wills v. Amerada Hess Corp., No. 98 CIV. 7126(RPP), 2002 WL 140542 (S.D.N.Y. Jan. 31, 2002), aff’d, 379 F.3d 32 (2d Cir. 2004) (Sotomayor, J.). In this Jones Act case, the plaintiff claimed that her husband’s exposure to benzene and polycyclic aromatic hydrocarbons on board ship caused his squamous cell lung cancer. Plaintiff’s expert witness relied heavily upon the IARC categorization of benzene as a “known” carcinogen, and an “oncogene” theory of causation that claimed there was no safe level of exposure because a single molecule could induce cancer. According to the plaintiff’s expert witness, the oncogene theory dispensed with the need to quantify exposure. Then Judge Sotomayor, citing Sutera, rejected plaintiff’s no-threshold theory, and the argument that exposure that exceeded OHSA permissible exposure level supported the causal claim.

Mancuso v. Consolidated Edison Co., 967 F. Supp. 1437, 1448 (S.D.N.Y. 1997) (“recommended or prescribed precautionary standards cannot provide legal causation”; “[f]ailure to meet regulatory standards is simply not sufficient” to establish liability)

In re Agent Orange Product Liab. Litig., 597 F. Supp. 740, 781 (E.D.N.Y. 1984) (Weinstein, J.) (“The distinction between avoidance of risk through regulation and compensation for injuries after the fact is a fundamental one.”), aff’d in relevant part, 818 F.2d 145 (2d Cir.1987), cert. denied sub nom. Pinkney v. Dow Chemical Co., 484 U.S. 1004 (1988). Judge Weinstein explained that regulatory action would not by itself support imposing liability for an individual plaintiff.  Id. at 782. “A government administrative agency may regulate or prohibit the use of toxic substances through rulemaking, despite a very low probability of any causal relationship.  A court, in contrast, must observe the tort law requirement that a plaintiff establish a probability of more than 50% that the defendant’s action injured him.” Id. at 785.

In re Ephedra Prods. Liab. Litig., 393 F. Supp. 2d 181, 189 (S.D.N.Y. 2005) (improvidently relying in part upon FDA ban despite “the absence of definitive scientific studies establishing causation”)

Third Circuit

Gates v. Rohm & Haas Co., 655 F.3d 255, 268 (3d Cir. 2011) (affirming the denial of class certification for medical monitoring) (‘‘plaintiffs could not carry their burden of proof for a class of specific persons simply by citing regulatory standards for the population as a whole’’).

In re Schering-Plough Corp. Intron/Temodar Consumer Class Action, 2009 WL 2043604, at *13 (D.N.J. July 10, 2009)(“[T]here is a clear and decisive difference between allegations that actually contest the safety or effectiveness of the Subject Drugs and claims that merely recite violations of the FDCA, for which there is no private right of action.”)

Rowe v. E.I. DuPont de Nemours & Co., Civ. No. 06-1810 (RMB), 2008 U.S. Dist. LEXIS 103528, *46-47 (D.N.J. Dec. 23, 2008) (rejecting reliance upon regulatory findings and risk assessments in which “the basic goal underlying risk assessments . . . is to determine a level that will protect the most sensitive members of the population.”)  (quoting David E. Eaton, “Scientific Judgment and Toxic Torts – A Primer in Toxicology for Judges and Lawyers,” 12 J.L. & Pol’y 5, 34 (2003) (“a number of protective, often ‘worst case’ assumptions . . . the resulting regulatory levels . . . generally overestimate potential toxicity levels for nearly all individuals.”)

Soldo v. Sandoz Pharms. Corp., 244 F. Supp. 2d 434, 543 (W.D. Pa. 2003) (finding FDA regulatory proceedings and adverse event reports not adequate or helpful in determining causation; the FDA “ordinarily does not attempt to prove that the drug in fact causes a particular adverse effect.”)Wade-Greaux v. Whitehall Laboratories, Inc., 874 F. Supp. 1441, 1464 (D.V.I.) (“assumption[s that] may be useful in a regulatory risk-benefit context … ha[ve] no applicability to issues of causation-in-fact”), aff’d, 46 F.3d 1120 (3d  Cir. 1994)

O’Neal v. Dep’t of the Army, 852 F. Supp. 327, 333 (M.D. Pa. 1994) (administrative risk figures are “appropriate for regulatory purposes in which the goal is to be particularly cautious [but] overstate the actual risk and, so, are inappropriate for use in determining” civil liability)

Fourth Circuit

Dunn v. Sandoz Pharmaceuticals Corp., 275 F. Supp. 2d 672, 684 (M.D.N.C. 2003) (FDA “risk benefit analysis” “does not demonstrate” causation in any particular plaintiff)

Yates v. Ford Motor Co., 113 F. Supp. 3d 841, 857 (E.D.N.C. 2015) (“statements from regulatory and official agencies … are not bound by standards for causation found in toxic tort law”)

Meade v. Parsley, No. 2:09-cv-00388, 2010 U.S. Dist. LEXIS 125217, * 25 (S.D.W. Va. Nov. 24, 2010) (‘‘Inasmuch as the cost-benefit balancing employed by the FDA differs from the threshold standard for establishing causation in tort actions, this court likewise concludes that the FDA-mandated [black box] warnings cannot establish general causation in this case.’’)

Rhodes v. E.I. du Pont de Nemours & Co., 253 F.R.D. 365, 377 (S.D. W.Va. 2008) (rejecting the relevance of regulatory assessments, which are precautionary and provide no information about actual risk).

Fifth Circuit

Moore v. Ashland Chemical Co., 126 F.3d 679, 708 (5th Cir. 1997) (holding that expert witness could rely upon a material safety data sheet (MSDS) because mandated by the Hazard Communication Act, 29 C.F.R. § 1910.1200), vacated 151 F.3d 269 (5th Cir. 1998) (affirming trial court’s exclusion of expert witness who had relied upon MSDS).

Johnson v. Arkema Inc., 685 F.3d 452, 464 (5th Cir. 2012) (per curiam) (affirming exclusion of expert witness who upon regulatory pronouncements; noting the precautionary nature of such statements, and the absence of specificity for the result claimed at the exposures experienced by plaintiff)

Allen v. Pennsylvania Eng’g Corp., 102 F.3d 194, 198-99 (5th Cir. 1996) (“Scientific knowledge of the harmful level of exposure to a chemical, plus knowledge that the plaintiff was exposed to such quantities, are minimal facts necessary to sustain the plaintiffs’ burden in a toxic tort case”; regulatory agencies, charged with protecting public health, employ a lower standard of proof in promulgating regulations than that used in tort cases). The Allen court explained that it was “also unpersuaded that the “weight of the evidence” methodology these experts use is scientifically acceptable for demonstrating a medical link. . . .  Regulatory and advisory bodies. . .utilize a “weight of the evidence” method to assess the carcinogenicity of various substances in human beings and suggest or make prophylactic rules governing human exposure.  This methodology results from the preventive perspective that the agencies adopt in order to reduce public exposure to harmful substances.  The agencies’ threshold of proof is reasonably lower than that appropriate in tort law, which traditionally makes more particularized inquiries into cause and effect and requires a plaintiff to prove that it is more likely than not that another individual has caused him or her harm.” Id.

Burst v. Shell Oil Co., C. A. No. 14–109, 2015 WL 3755953, *8 (E.D. La. June 16, 2015) (explaining Fifth Circuit’s rejection of regulatory “weight of the evidence” approaches to evaluating causation)

Sprankle v. Bower Ammonia & Chem. Co., 824 F.2d 409, 416 (5th Cir. 1987) (affirmed Rule 403 exclusion evidence of OSHA violations in claim of respiratory impairment in a non-employee who experienced respiratory impairment after exposure to anhydrous ammonia; court found that the jury likely be confused by regulatory pronouncements)

Cano v. Everest Minerals Corp., 362 F. Supp. 2d 814, 825 (W.D. Tex. 2005) (noting that a product that “has been classified as a carcinogen by agencies responsible for public health regulations is not probative of” common-law specific causation) (finding that the linear no-threshold opinion of the plaintiffs’ expert witness, Malin Dollinger, lacked a satisfactory scientific basis)

Burleson v. Glass, 268 F. Supp. 2d 699, 717 (W.D. Tex. 2003) (“the mere fact that [the product] has been classified by certain regulatory organizations as a carcinogen is not probative on the issue of whether [plaintiff’s] exposure. . .caused his. . .cancers”), aff’d, 393 F.3d 577 (5th Cir. 2004)

Newton v. Roche Labs., Inc., 243 F. Supp. 2d 672, 677, 683 (W.D. Tex. 2002) (FDA’s precautionary decisions on labeling are not a determination of causation of specified adverse events) (“Although evidence of an association may … be important in the scientific and regulatory contexts…, tort law requires a higher standard of causation.”)

Molden v. Georgia Gulf Corp., 465 F. Supp. 2d 606, 611 (M.D. La. 2006) (“regulatory and advisory bodies make prophylactic rules governing human exposure based on proof that is reasonably lower than that appropriate in tort law”)

Sixth Circuit

Nelson v. Tennessee Gas Pipeline Co., 243 F.3d 244, 252-53 (6th Cir. 2001) (exposure above regulatory levels is insufficient to establish causation)

Stites v Sundstrand Heat Transfer, Inc., 660 F. Supp. 1516, 1525 (W.D. Mich. 1987) (rejecting use of regulatory standards to support claim of increased risk, noting the differences in goals and policies between regulation and litigation)

Mann v. CSX Transportation, Inc., case no. 1:07-Cv-3512, 2009 U.S. Dist. Lexis 106433 (N.D. Ohio Nov. 10, 2009) (rejecting expert testimony that relied upon EPA action levels, and V.A. compensation for dioxin exposure, as basis for medical monitoring opinions)

Baker v. Chevron USA, Inc., 680 F. Supp. 2d 865, 880 (S.D. Ohio 2010) (“[R]egulatory agencies are charged with protecting public health and thus reasonably employ a lower threshold of proof in promulgating their regulations than is used in tort cases.”) (“[t]he mere fact that Plaintiffs were exposed to [the product] in excess of mandated limits is insufficient to establish causation”; rejecting Dr. Dahlgren’s opinion and its reliance upon a “one-hit” or “no threshold” theory of causation in which exposure to one molecule of a cancer-causing agent has some finite possibility of causing a genetic mutation leading to cancer, a theory that may be accepted for purposes of setting regulatory standards, but not as reliable scientific knowledge)

Adams v. Cooper Indus., 2007 WL 2219212 at *7 (E.D. KY 2007).

Seventh Circuit

Wood v. Textron, Inc., No. 3:10 CV 87, 2014 U.S. Dist. LEXIS 34938 (N.D. Ind. Mar. 17, 2014); 2014 U.S. Dist. LEXIS 141593, at *11 (N.D. Ind. Oct. 3, 2014), aff’d, 807 F.3d 827 (7th Cir. 2015). Dahlgren based his opinions upon the children’s water supply containing vinyl chloride in excess of regulatory levels set by state and federal agencies, including the EPA. Similarly, Ryer-Powder relied upon exposure levels’ exceeding regulatory permissible limits for her causation opinions. The district court, with the approval now of the Seventh Circuit would have none of this nonsense. Exceeding governmental regulatory exposure limits does not prove causation. The con-compliance does not help the fact finder without knowing “the specific dangers” that led the agency to set the permissible level, and thus the regulations are not relevant at all without this information. Even with respect to specific causation, the regulatory infraction may be weak or null evidence for causation. (citing Cunningham v. Masterwear Corp., 569 F.3d 673, 674–75 (7th Cir. 2009)

Eighth Circuit

Glastetter v. Novartis Pharms. Corp., 107 F. Supp. 2d 1015, 1036 (E.D. Mo. 2000) (“[T]he [FDA’s] statement fails to affirmatively state that a connection exists between [the drug] and the type of injury in this case.  Instead, it states that the evidence received by the FDA calls into question [drug’s] safety, that [the drug] may be an additional risk factor. . .and that the FDA had new evidence suggesting that therapeutic use of [the drug] may lead to serious adverse experiences.  Such language does not establish that the FDA had concluded that [the drug] can cause [the injury]; instead, it indicates that in light of the limited social utility of [the drug for the use at issue] and the reports of possible adverse effects, the drug should no longer be used for that purpose.”) (emphasis in original), aff’d, 252 F.3d 986, 991 (8th Cir. 2001) (FDA’s precautionary decisions on labeling are not a determination of causation of specified adverse events; “methodology employed by a government agency results from the preventive perspective that the agencies adopt”)( “The FDA will remove drugs from the marketplace upon a lesser showing of harm to the public than the preponderance-of-the-evidence or the more-like-than-not standard used to assess tort liability . . . . [Its] decision that [the drug] can cause [the injury] is unreliable proof of medical causation.”), aff’d, 252 F.3d 986 (8th Cir. 2001)

Wright v. Willamette Indus., Inc., 91 F.3d 1105, 1107 (8th Cir. 1996) (rejecting claim that plaintiffs were not required to show individual exposure levels to formaldehyde from wood particles). The Wright court elaborated upon the difference between adjudication and regulation of harm:

“Whatever may be the considerations that ought to guide a legislature in its determination of what the general good requires, courts and juries, in deciding cases, traditionally make more particularized inquiries into matters of cause and effect.  Actions in tort for damages focus on the question of whether to transfer money from one individual to another, and under common-law principles (like the ones that Arkansas law recognizes) that transfer can take place only if one individual proves, among other things, that it is more likely than not that another individual has caused him or her harm.  It is therefore not enough for a plaintiff to show that a certain chemical agent sometimes causes the kind of harm that he or she is complaining of.  At a minimum, we think that there must be evidence from which the factfinder can conclude that the plaintiff was exposed to levels of that agent that are known to cause the kind of harm that the plaintiff claims to have suffered. See Abuan v. General Elec. Co., 3 F.3d at 333.  We do not require a mathematically precise table equating levels of exposure with levels of harm, but there must be evidence from which a reasonable person could conclude that a defendant’s emission has probablycaused a particular plaintiff the kind of harm of which he or she complains before there can be a recovery.”

Gehl v. Soo Line RR, 967 F.2d 1204, 1208 (8th Cir. 1992).

Nelson v. Am. Home Prods. Corp., 92 F. Supp. 2d 954, 958 (W.D. Mo. 2000) (FDA’s precautionary decisions on labeling are not a determination of causation of specified adverse events)

National Bank of Commerce v. Associated Milk Producers, Inc., 22 F. Supp. 2d 942, 961 (E.D.Ark. 1998), aff’d, 191 F.3d 858 (8th Cir. 1999) 

Junk v. Terminix Internat’l Co., 594 F. Supp. 2d 1062, 1071 (S.D. Iowa 2008) (“government agency regulatory standards are irrelevant to [plaintiff’s] burden of proof in a toxic tort cause of action because of the agency’s preventative perspective”)

Ninth Circuit

Henrickson v. ConocoPhillips Co., 605 F. Supp. 2d 1142, 1156 (E.D. Wash. 2009) (excluding expert witness causation opinions in case involving claims that benzene exposure caused leukemia) 

Lopez v. Wyeth-Ayerst Labs., Inc., 1998 WL 81296, at *2 (9th Cir. Feb. 25, 1998) (FDA’s precautionary decisions on labeling are not a determination of causation of specified adverse events)

In re Epogen & Aranesp Off-Label Marketing & Sales Practices Litig., 2009 WL 1703285, at *5 (C.D. Cal. June 17, 2009) (“have not been proven” allegations are an improper “FDA approval” standard; the FDA’s determination to require warning changes without establishing causation is established does not permit a court or jury, bound by common-law standards, to impose such a duty to warn when common-law causation requirements are not met).

In re Hanford Nuclear Reservation Litig., 1998 U.S. Dist. Lexis 15028 (E.D. Wash. 1998) (radiation and chromium VI), rev’d on other grounds, 292 F.3d 1124 (9th Cir. 2002).

Tenth Circuit

Hollander v. Shandoz Pharm. Corp., 95 F. Supp. 2d 1230, 1239 (W.D. Okla. 2000) (distinguishing FDA’s threshold of proof as lower than appropriate in tort law), aff’d in relevant part, 289 F.3d 1193, 1215 (10th Cir. 2002)

Mitchell v. Gencorp Inc., 165 F.3d 778, 783 n.3 (10th Cir. 1999) (benzene and CML) (quoting Allen, 102 F.3d at 198) (state administrative finding that product was a carcinogen was based upon lower administrative standard than tort standard) (“The methodology employed by a government agency “results from the preventive perspective that the agencies adopt in order to reduce public exposure to harmful substances.  The agencies’ threshold of proof is reasonably lower than that appropriate in tort law, which traditionally makes more particularized inquiries into cause and effect and requires a plaintiff to prove it is more likely than not that another individual has caused him or her harm.”)

In re Breast Implant Litig., 11 F. Supp. 2d 1217, 1229 (D.Colo. 1998)

Johnston v. United States, 597 F. Supp. 374, 393-394 (D. Kan.1984) (noting that the linear no-threshold hypothesis is based upon a prudent assumption designed to overestimate risk; speculative hypotheses are not appropriate in determining whether one person has harmed another)

Eleventh Circuit

Rider v. Sandoz Pharmaceuticals Corp., 295 F.3d 1194, 1201 (11th Cir. 2002) (FDA may take regulatory action, such as revising warning labels or withdrawing drug from the market ‘‘upon a lesser showing of harm to the public than the preponderance-of-the-evidence or more-likely-than-not standard used to assess tort liability’’) (“A regulatory agency such as the FDA may choose to err on the side of caution. Courts, however, are required by the Daubert trilogy to engage in objective review of the evidence to determine whether it has sufficient scientific basis to be considered reliable.”)

McClain v. Metabolife Internat’l, Inc., 401 F.3d 1233, 1248-1250 (11th Cir. 2005) (ephedra) (allowing that regulators “may pay heed to any evidence that points to a need for caution,” and apply “a much lower standard than that which is demanded by a court of law”) (“[U]se of FDA data and recommendations raises a more subtle methodological issue in a toxic tort case. The issue involves identifying and contrasting the type of risk assessment that a government agency follows for establishing public health guidelines versus an expert analysis of toxicity and causation in a toxic tort case.”)

In re Seroquel Products Liab. Litig., 601 F. Supp. 2d 1313, 1315 (M.D. Fla. 2009) (noting that administrative agencies “impose[] different requirements and employ[] different labeling and evidentiary standards” because a “regulatory system reflects a more prophylactic approach” than the common law)

Siharath v. Sandoz Pharmaceuticals Corp., 131 F. Supp. 2d 1347, 1370 (N.D. Ga. 2001) (“The standard by which the FDA deems a drug harmful is much lower than is required in a court of law.  The FDA’s lesser standard is necessitated by its prophylactic role in reducing the public’s exposure to potentially harmful substances.”), aff’d, 295 F.3d 1194 330 (11th Cir. 2002)

In re Accutane Products Liability, 511 F.Supp.2d 1288, 1291-92 (M.D. Fla. 2007)(acknowledging that regulatory risk assessments are not necessarily realistic in human populations because they are often based upon animal studies, and that the important differences between experimental animals and humans are substantial in various health outcomes).

Kilpatrick v. Breg, Inc., 2009 WL 2058384 at * 6-7 (S.D. Fla. 2009) (excluding plaintiff’s expert witness), aff’d, 613 F.3d 1329 (11th Cir. 2010)

District of Columbia Circuit

Ethyl Corp. v. E.P.A., 541 F.2d 1, 28 & n. 58 (D.C. Cir. 1976) (detailing the precautionary nature of agency regulations that may be based upon suspicions)

STATE COURTS

Arizona

Lofgren v. Motorola, 1998 WL 299925 (Ariz. Super. Ct. 1998) (finding plaintiffs’ expert witnesses’ testimony that TCE caused cancer to be not generally accepted; “it is appropriate public policy for health organizations such as IARC and the EPA to make judgments concerning the health and safety of the population based on evidence which would be less than satisfactory to support a specific plaintiff’s tort claim for damages in a court of law”)

Colorado

Salazar v. American Sterilizer Co., 5 P.3d 357 (Colo. Ct. App. 2000) (allowing testimony about harmful ethylene oxide exposure based upon OSHA regulations)

Georgia

Butler v. Union Carbide Corp., 712 S.E.2d 537, 552 & n.37 (Ga. App. 2011) (distinguishing risk assessment from causation assessment; citing the New York Court of Appeals decision in Parker for correctly rejecting reliance on regulatory pronouncements for causation determinations)

Illinois

La Salle Nat’l Bank v. Malik, 705 N.E.2d 938 (Ill. App. 3d) (reversing trial court’s exclusion of OSHA PEL for ethylene oxide), writ pet’n den’d, 714 N.E.2d 527 (Ill. 2d 1999)

New York

Parker v. Mobil Oil Corp., 7 N.Y.3d 434, 450, 857 N.E.2d 1114, 1122, 824 N.Y.S.2d 584 (N.Y. 2006) (noting that regulatory agency standards usually represent precautionary principle efforts deliberately to err on side of prevention; “standards promulgated by regulatory agencies as protective measures are inadequate to demonstrate legal causation.” 

In re Bextra & Celebrex, 2008 N.Y. Misc. LEXIS 720, *20, 239 N.Y.L.J. 27 (2008) (characterizing FDA Advisory Panel recommendations as regulatory standard and protective measure).

Juni v. A.O. Smith Water Products Co., 48 Misc. 3d 460, 11 N.Y.S.3d 416, 432, 433 (N.Y. Cty. 2015) (“the reports and findings of governmental agencies [declaring there to be no safe dose of asbestos] are irrelevant as they constitute insufficient proof of causation”), aff’d, 32 N.Y.3d 1116, 116 N.E.3d 75, 91 N.Y.S.3d 784 (2018)

Ohio

Valentine v. PPG Industries, Inc., 821 N.E.2d 580, 597-98 (Ohio App. 2004), aff’d, 850 N.E.2d 683 (Ohio 2006). 

Pennsylvania

Betz v. Pneumo Abex LLC, 44 A. 3d 27 (Pa. 2012).

Texas

Borg-Warner Corp., 232 S.W.3d 765, 770 (Tex. 2007)

Exxon Corp. v. Makofski, 116 S.W.3d 176, 187-88 (Tex. App. 2003) (describing “standards used by OSHA [and] the EPA” as inadequate for causal determinations)


[1] Michael D. Green, D. Michal Freedman, and Leon Gordis, “Reference Guide on Epidemiology,” in Reference Manual on Scientific Evidence 549, 627 (3d ed. 2011).

[2] Margaret A. Berger, “The Supreme Court’s Trilogy on the Admissibility of Expert Testimony,” in Reference Manual On Scientific Evidence at 33 (Fed. Jud. Center 2d. ed. 2000).

[3] Margaret A. Berger, “Introduction to the Symposium,” 12 J. L. & Pol’y 1 (2003). Professor Berger described the symposium as a “felicitous outgrowth of a grant from the Common Benefit Trust established in the Silicone Breast Implant Products Liability Litigation to hold a series of conferences at Brooklyn Law School.” Id. at 1. Ironically, that “Trust” was nothing more than the walking-around money of plaintiffs’ lawyers from the Silicone-Gel Breast Implant MDL 926. Although Professor Berger was often hostile the causation requirement in tort law, her symposium included some well-qualified scientists who amplified her point from the Reference Manual about the divide between regulatory risk assessment and scientific causal assessments.

[4] David L. Eaton, Scientific Judgment and Toxic Torts- A Primer in Toxicology for Judges and Lawyers, 12 J.L. & Pol’y 5, 36 (2003). See also Joseph V. Rodricks and Susan H. Rieth, “Toxicological risk assessment in the courtroom: are available methodologies suitable for evaluating toxic tort and product liability claims?” 27 Regul. Toxicol. & Pharmacol. 21, 27 (1998) (“The public health-oriented resolution of scientific uncertainty [used by regulators] is not especially helpful to the problem faced by a court.”)

[5] EPA “Guidelines for Carcinogen Risk Assessment” at 13 (1986).

[6] The approach is set out in FDA, M7 (R1) Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk: Guidance for Industry (2018) [FDA M7]. This FDA guidance is essentially an adoption of the M7 document of the Expert Working Group (Multidisciplinary) of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH).

[7] FDA M7 at 3.

[8] FDA M7 at 5.

[9] FDA M7 at 5 (emphasis added).

[10] See Labeling of Diphenhydramine Containing Drug Products for Over-the-Counter Human Use, 67 Fed. Reg. 72,555, at 72,556 (Dec. 6, 2002) (“FDA’s decision to act in an instance such as this one need not meet the standard of proof required to prevail in a private tort action. . .. To mandate a warning or take similar regulatory action, FDA need not show, nor do we allege, actual causation.”) (citing Glastetter).

[11] FDA M7 at “Acceptable Intakes in Relation to Less-Than-Lifetime (LTL) Exposure (7.3).”

[12] FDA M7 at 12 (“Mutagenic Impurities With Evidence for a Practical Threshold (7.2.2)”).

Confounded by Confounding in Unexpected Places

December 12th, 2021

In assessing an association for causality, the starting point is “an association between two variables, perfectly clear-cut and beyond what we would care to attribute to the play of chance.”[1] In other words, before we even embark on consideration of Bradford Hill’s nine considerations, we should have ruled out chance, bias, and confounding as an explanation for the claimed association.[2]

Although confounding is sometimes considered as a type of systematic bias, its importance warrants its own category. Historically, courts have been rather careless in addressing confounding. The Supreme Court, in a case decided before Daubert and the statutory modifications to Rule 702, ignored the role of confounding in a multiple regression model used to support racial discrimination claims. In language that would be reprised many times to avoid and evade the epistemic demands of Rule 702, the Court held, in Bazemore, that the omission of variables in multiple regression models raises an issue that affects “the  analysis’ probativeness, not its admissibility.”[3]

When courts have not ignored confounding,[4] they have sidestepped its consideration by imparting magical abilities to confidence intervals to take care of problem posed by lurking variables.[5]

The advent of the Reference Manual on Scientific Manual allowed a ray of hope to shine on health effects litigation. Several important cases have been decided by judges who have taken note of the importance of assessing studies for confounding.[6] As a new, fourth edition of the Manual is being prepared, its editors and authors should not lose sight of the work that remains to be done.

The Third Edition of the Federal Judicial Center’s and the National Academies of Science, Engineering & Medicine’s Reference Manual on Scientific Evidence (RMSE3d 2011) addressed confounding in several chapters, not always consistently. The chapter on statistics defined “confounder” in terms of correlation between both the independent and dependent variables:

“[a] confounder is correlated with the independent variable and the dependent variable. An association between the dependent and independent variables in an observational study may not be causal, but may instead be due to confounding”[7]

The chapter on epidemiology, on the other hand, defined a confounder as a risk factor for both the exposure and disease outcome of interest:

“A factor that is both a risk factor for the disease and a factor associated with the exposure of interest. Confounding refers to a situation in which an association between an exposure and outcome is all or partly the result of a factor that affects the outcome but is unaffected by the exposure.”[8]

Unfortunately, the epidemiology chapter never defined “risk factor.” The term certainly seems much less neutral than a “correlated” variable, which lacks any suggestion of causality. Perhaps there is some implied help from the authors of the epidemiology chapter when they described a case of confounding by “known causal risk factors,” which suggests that some risk factors may not be causal.[9] To muck up the analysis, however, the epidemiology chapter went on to define “risk” as “[a] probability that an event will occur (e.g., that an individual will become ill or die within a stated period of time or by a certain age).”[10]

Both the statistics and the epidemiology chapters provide helpful examples of confounding and speak to the need for excluding confounding as the basis for an observed association. The statistics chapter, for instance, described confounding as a threat to “internal validity,”[11] and the need to inquire whether the adjustments in multivariate studies were “sensible and sufficient.”[12]

The epidemiology chapter in one passage instructed that when “an association is uncovered, further analysis should be conducted to assess whether the association is real or a result of sampling error, confounding, or bias.[13] Elsewhere in the same chapter, the precatory becomes mandatory.[14]

Legally Unexplored Source of Substantial Confounding

As the Reference Manual implies, attempting to control for confounding is not adequate.  The controlling must be carefully and sufficiently done. Under the heading of sufficiency and due care, there are epidemiologic studies that purport to control for confounding, but fail rather dramatically. The use of administrative databases, whether based upon national healthcare or insurance claims, has become a common place in chronic disease epidemiology. Their large size obviates many concerns about power to detect rare disease outcomes. Unfortunately, there is often a significant threat to the validity of such studies, which are based upon data sets that characterize patients as diabetic, hypertensive, obese, or smokers vel non. By dichotomizing what are continuous variables, the categorization extracts a significant price in multivariate models used in epidemiology.

Of course, physicians frequently create guidelines for normal versus abnormal, and these divisions or categories show up in medical records, in databases, and ultimately in epidemiologic studies. The actual measurements are not always available, and the use of a categorical variable may appear to simplify the statistical analysis of the dataset. Unfortunately, the results can be quite misleading. Consider the measurements of blood pressure in a study that is evaluating whether an exposure variable (such as medication use or environmental contaminant) is associated with an outcome such as cardiovascular or renal disease. Hypertension, if present, would clearly be a confounder, but the use of a categorical variable for hypertension would greatly undermine the validity of the study. If many of the study participants with hypertension had their condition well controlled by medication, then the categorical variable will dilute the adjustment for the role of hypertension in driving the association between the exposure and outcome variables of interest. Even if none of the hypertensive patients had good control, the reduction of all hypertension to a category, rather than a continuous measurement, is a path of the loss of information and the creation of bias.

Almost 40 years ago, Jacob Cohen showed that dichotomization of continuous variables results in a loss of power.[15] Twenty years later, Peter Austin showed in a Monte Carlo simulation that categorizing a continuous variable in a logistic regression results in inflating the rate of finding false positive associations.[16] The type I (false-positive) error rates increases with sample size, with increasing correlation between the confounding variable and outcome of interest, and the number of categories used for the continuous variables. Of course, the national databases often have huge sample sizes, which only serves to increase the bias from the use of categorical variables for confounding variables.

The late Douglas Altman, who did so much to steer the medical literature toward greater validity, warned that dichotomizing continuous variables was known to cause loss of information, statistical power, and reliability in medical research.[17]

In the field of pharmaco-epidemiology, the bias created by dichotomization of a continous variable is harmful from both the perspective of statistical estimation and hypothesis testing.[18] While readers are misled into believing that the study adjusts for important co-variates, the study will have lost information and power, with the result of presenting false-positive results that have the false-allure of a fully adjusted model. Indeed, this bias from inadequate control of confounding infects several pending pharmaceutical multi-district litigations.


Supreme Court

General Electric Co. v. Joiner, 522 U.S. 136, 145-46 (1997) (holding that an expert witness’s reliance on a study was misplaced when the subjects of the study “had been exposed to numerous potential carcinogens”)

First Circuit

Bricklayers & Trowel Trades Internat’l Pension Fund v. Credit Suisse Securities (USA) LLC, 752 F.3d 82, 89 (1st Cir. 2014) (affirming exclusion of expert witness who failed to account for confounding in event studies), aff’g 853 F. Supp. 2d 181, 188 (D. Mass. 2012)

Second Circuit

Wills v. Amerada Hess Corp., 379 F.3d 32, 50 (2d Cir. 2004) (holding expert witness’s specific causation opinion that plaintiff’s squamous cell carcinoma had been caused by polycyclic aromatic hydrocarbons was unreliable, when plaintiff had smoked and drunk alcohol)

Deutsch v. Novartis Pharms. Corp., 768 F.Supp. 2d 420, 432 (E.D.N.Y. 2011) (“When assessing the reliability of a epidemiologic study, a court must consider whether the study adequately accounted for “confounding factors.”)

Schwab v. Philip Morris USA, Inc., 449 F. Supp. 2d 992, 1199–1200 (E.D.N.Y. 2006), rev’d on other grounds, 522 F.3d 215 (2d Cir. 2008) (describing confounding in studies of low-tar cigarettes, where authors failed to account for confounding and assessing healthier life styles in users)

Third Circuit

In re Zoloft Prods. Liab. Litig., 858 F.3d 787, 793 (3d Cir. 2017) (affirming exclusion of causation expert witness)

Magistrini v. One Hour Martinizing Dry Cleaning, 180 F. Supp. 2d 584, 591 (D.N.J. 2002), aff’d, 68 Fed. Appx. 356 (3d Cir. 2003)(bias, confounding, and chance must be ruled out before an association  may be accepted as showing a causal association)

Soldo v. Sandoz Pharms. Corp., 244 F. Supp. 2d 434 (W.D.Pa. 2003) (excluding expert witnesses in Parlodel case; noting that causality assessments and case reports fail to account for confounding)

Wade-Greaux v. Whitehall Labs., Inc., 874 F. Supp. 1441 (D.V.I. 1994) (unanswered questions about confounding required summary judgment  against plaintiff in Primatene Mist birth defects case)

Fifth Circuit

Knight v. Kirby Inland Marine, Inc., 482 F.3d 347, 353 (5th Cir. 2007) (affirming exclusion of expert witnesses) (“Of all the organic solvents the study controlled for, it could not determine which led to an increased risk of cancer …. The study does not provide a reliable basis for the opinion that the types of chemicals appellants were exposed to could cause their particular injuries in the general population.”)

Burst v. Shell Oil Co., C. A. No. 14–109, 2015 WL 3755953, *7 (E.D. La. June 16, 2015) (excluding expert witness causation opinion that failed to account for other confounding exposures that could have accounted for the putative association), aff’d, 650 F. App’x 170 (5th Cir. 2016)

LeBlanc v. Chevron USA, Inc., 513 F. Supp. 2d 641, 648-50 (E.D. La. 2007) (excluding expert witness testimony that purported to show causality between plaintiff’s benzene ezposure and myelofibrosis), vacated, 275 Fed. App’x 319 (5th Cir. 2008) (remanding case for consideration of new government report on health effects of benzene)

Castellow v. Chevron USA, 97 F. Supp. 2d 780 (S.D. Tex. 2000) (discussing confounding in passing; excluding expert witness causation opinion in gasoline exposure AML case)

Kelley v. American Heyer-Schulte Corp., 957 F. Supp. 873 (W.D. Tex. 1997) (confounding in breast implant studies)

Sixth Circuit

Pluck v. BP Oil Pipeline Co., 640 F.3d 671 (6th Cir. 2011) (affirming exclusion of specific causation opinion that failed to rule out confounding factors)

Nelson v. Tennessee Gas Pipeline Co., 243 F.3d 244, 252-54 (6th Cir. 2001) (rewrite: expert’s failure to account for confounding factors in cohort study of alleged PCB exposures rendered his opinion unreliable)

Turpin v. Merrell Dow Pharms., Inc., 959 F. 2d 1349, 1355 -57 (6th Cir. 1992) (discussing failure of some studies to evaluate confounding)

Adams v. Cooper Indus. Inc., 2007 WL 2219212, 2007 U.S. Dist. LEXIS 55131 (E.D. Ky. 2007) (differential diagnosis includes ruling out confounding causes of plaintiffs’ disease).

Seventh Circuit

People Who Care v. Rockford Bd. of Educ., 111 F.3d 528, 537–38 (7th Cir. 1997) (noting importance of considering role of confounding variables in educational achievement);

Caraker v. Sandoz Pharms. Corp., 188 F. Supp. 2d 1026, 1032, 1036 (S.D. Ill 2001) (noting that “the number of dechallenge/rechallenge reports is too scant to reliably screen out other causes or confounders”)

Eighth Circuit

Penney v. Praxair, Inc., 116 F.3d 330, 333-334 (8th Cir. 1997) (affirming exclusion of expert witness who failed to account of the confounding effects of age, medications, and medical history in interpreting PET scans)

Marmo v. Tyson Fresh Meats, Inc., 457 F.3d 748, 758 (8th Cir. 2006) (affirming exclusion of specific causation expert witness opinion)

Ninth Circuit

Coleman v. Quaker Oats Co., 232 F.3d 1271, 1283 (9th Cir. 2000) (p-value of “3 in 100 billion” was not probative of age discrimination when “Quaker never contend[ed] that the disparity occurred by chance, just that it did not occur for discriminatory reasons. When other pertinent variables were factored in, the statistical disparity diminished and finally disappeared.”)

In re Viagra & Cialis Prods. Liab. Litig., 424 F.Supp. 3d 781 (N.D. Cal. 2020) (excluding causation opinion on grounds including failure to account properly for confounding)

Avila v. Willits Envt’l Remediation Trust, 2009 WL 1813125, 2009 U.S. Dist. LEXIS 67981 (N.D. Cal. 2009) (excluding expert witness opinion that failed to rule out confounding factors of other sources of exposure or other causes of disease), aff’d in relevant part, 633 F.3d 828 (9th Cir. 2011)

In re Phenylpropanolamine Prods. Liab. Litig., 289 F.Supp.2d 1230 (W.D.Wash. 2003) (ignoring study validity in a litigation arising almost exclusively from a single observational study that had multiple internal and external validity problems; relegating assessment of confounding to cross-examination)

In re Bextra and Celebrex Marketing Sales Practice, 524 F. Supp. 2d 1166, 1172 – 73 (N.D. Calif. 2007) (discussing invalidity caused by confounding in epidemiologic studies)

In re Silicone Gel Breast Implants Products Liab. Lit., 318 F.Supp. 2d 879, 893 (C.D.Cal. 2004) (observing that controlling for potential confounding variables is required, among other findings, before accepting epidemiologic studies as demonstrating causation).

Henricksen v. ConocoPhillips Co., 605 F. Supp. 2d 1142 (E.D. Wash. 2009) (noting that confounding must be ruled out)

Valentine v. Pioneer Chlor Alkali Co., Inc., 921 F. Supp. 666 (D. Nev. 1996) (excluding plaintiffs’ expert witnesses, including Dr. Kilburn, for reliance upon study that failed to control for confounding)

Tenth Circuit

Hollander v. Sandoz Pharms. Corp., 289 F.3d 1193, 1213 (10th Cir. 2002) (noting importance of accounting for confounding variables in causation of stroke)

In re Breast Implant Litig., 11 F. Supp. 2d 1217, 1233 (D. Colo. 1998) (alternative explanations, such confounding, should be ruled out before accepting causal claims).

Eleventh Circuit

In re Abilify (Aripiprazole) Prods. Liab. Litig., 299 F.Supp. 3d 1291 (N.D.Fla. 2018) (discussing confounding in studies but credulously accepting challenged explanations from David Madigan) (citing Bazemore, a pre-Daubert, decision that did not address a Rule 702 challenge to opinion testimony)

District of Columbia Circuit

American Farm Bureau Fed’n v. EPA, 559 F.3d 512 (D.C. Cir. 2009) (noting that data relied upon in setting particulate matter standards addressing visibility should avoid the confounding effects of humidity)

STATES

Delaware

In re Asbestos Litig., 911 A.2d 1176 (New Castle Cty., Del. Super. 2006) (discussing confounding; denying motion to exclude plaintiffs’ expert witnesses’ chrysotile causation opinions)

Minnesota

Goeb v. Tharaldson, 615 N.W.2d 800, 808, 815 (Minn. 2000) (affirming exclusion of Drs. Janette Sherman and Kaye Kilburn, in Dursban case, in part because of expert witnesses’ failures to consider confounding adequately).

New Jersey

In re Accutane Litig., 234 N.J. 340, 191 A.3d 560 (2018) (affirming exclusion of plaintiffs’ expert witnesses’ causation opinions; deprecating reliance upon studies not controlled for confounding)

In re Proportionality Review Project (II), 757 A.2d 168 (N.J. 2000) (noting the importance of assessing the role of confounders in capital sentences)

Grassis v. Johns-Manville Corp., 591 A.2d 671, 675 (N.J. Super. Ct. App. Div. 1991) (discussing the possibility that confounders may lead to an erroneous inference of a causal relationship)

Pennsylvania

Porter v. SmithKline Beecham Corp., No. 3516 EDA 2015, 2017 WL 1902905 (Pa. Super. May 8, 2017) (affirming exclusion of expert witness causation opinions in Zoloft birth defects case; discussing the importance of excluding confounding)

Tennessee

McDaniel v. CSX Transportation, Inc., 955 S.W.2d 257 (Tenn. 1997) (affirming trial court’s refusal to exclude expert witness opinion that failed to account for confounding)


[1] Austin Bradford Hill, “The Environment and Disease: Association or Causation?” 58 Proc. Royal Soc’y Med. 295, 295 (1965) (emphasis added).

[2] See, e.g., David A. Grimes & Kenneth F. Schulz, “Bias and Causal Associations in Observational Research,” 359 The Lancet 248 (2002).

[3] Bazemore v. Friday, 478 U.S. 385, 400 (1986) (reversing Court of Appeal’s decision that would have disallowed a multiple regression analysis that omitted important variables). Buried in a footnote, the Court did note, however, that “[t]here may, of course, be some regressions so incomplete as to be inadmissible as irrelevant; but such was clearly not the case here.” Id. at 400 n.10. What the Court missed, of course, is that the regression may be so incomplete as to be unreliable or invalid. The invalidity of the regression in Bazemore does not appear to have been raised as an evidentiary issue under Rule 702. None of the briefs in the Supreme Court or the judicial opinions cited or discussed Rule 702.

[4]Confounding in the Courts” (Nov. 2, 2018).

[5] See, e.g., Brock v. Merrill Dow Pharmaceuticals, Inc., 874 F.2d 307, 311-12 (5th Cir. 1989) (“Fortunately, we do not have to resolve any of the above questions [as to bias and confounding], since the studies presented to us incorporate the possibility of these factors by the use of a confidence interval.”). This howler has been widely acknowledged in the scholarly literature. See David Kaye, David Bernstein, and Jennifer Mnookin, The New Wigmore – A Treatise on Evidence: Expert Evidence § 12.6.4, at 546 (2d ed. 2011); Michael O. Finkelstein, Basic Concepts of Probability and Statistics in the Law 86-87 (2009) (criticizing the blatantly incorrect interpretation of confidence intervals by the Brock court).

[6]On Praising Judicial Decisions – In re Viagra” (Feb. 8, 2021); See “Ruling Out Bias and Confounding Is Necessary to Evaluate Expert Witness Causation Opinions” (Oct. 28, 2018); “Rule 702 Requires Courts to Sort Out Confounding” (Oct. 31, 2018).

[7] David H. Kaye and David A. Freedman, “Reference Guide on Statistics,” in RMSE3d 211, 285 (3ed 2011). 

[8] Michael D. Green, D. Michal Freedman, and Leon Gordis, “Reference Guide on Epidemiology,” in RMSE3d 549, 621.

[9] Id. at 592.

[10] Id. at 627.

[11] Id. at 221.

[12] Id. at 222.

[13] Id. at 567-68 (emphasis added).

[14] Id. at 572 (describing chance, bias, and confounding, and noting that “[b]efore any inferences about causation are drawn from a study, the possibility of these phenomena must be examined”); id. at 511 n.22 (observing that “[c]onfounding factors must be carefully addressed”).

[15] Jacob Cohen, “The cost of dichotomization,” 7 Applied Psychol. Measurement 249 (1983).

[16] Peter C. Austin & Lawrence J. Brunner, “Inflation of the type I error rate when a continuous confounding variable is categorized in logistic regression analyses,” 23 Statist. Med. 1159 (2004).

[17] See, e.g., Douglas G. Altman & Patrick Royston, “The cost of dichotomising continuous variables,” 332 Brit. Med. J. 1080 (2006); Patrick Royston, Douglas G. Altman, and Willi Sauerbrei, “Dichotomizing continuous predictors in multiple regression: a bad idea,” 25 Stat. Med. 127 (2006). See also Robert C. MacCallum, Shaobo Zhang, Kristopher J. Preacher, and Derek D. Rucker, “On the Practice of Dichotomization of Quantitative Variables,” 7 Psychological Methods 19 (2002); David L. Streiner, “Breaking Up is Hard to Do: The Heartbreak of Dichotomizing Continuous Data,” 47 Can. J. Psychiatry 262 (2002); Henian Chen, Patricia Cohen, and Sophie Chen, “Biased odds ratios from dichotomization of age,” 26 Statist. Med. 3487 (2007); Carl van Walraven & Robert G. Hart, “Leave ‘em Alone – Why Continuous Variables Should Be Analyzed as Such,” 30 Neuroepidemiology 138 (2008); O. Naggara, J. Raymond, F. Guilbert, D. Roy, A. Weill, and Douglas G. Altman, “Analysis by Categorizing or Dichotomizing Continuous Variables Is Inadvisable,” 32 Am. J. Neuroradiol. 437 (Mar 2011); Neal V. Dawson & Robert Weiss, “Dichotomizing Continuous Variables in Statistical Analysis: A Practice to Avoid,” Med. Decision Making 225 (2012); Phillippa M Cumberland, Gabriela Czanner, Catey Bunce, Caroline J Doré, Nick Freemantle, and Marta García-Fiñana, “Ophthalmic statistics note: the perils of dichotomising continuous variables,” 98 Brit. J. Ophthalmol. 841 (2014).

[18] Valerii Fedorov, Frank Mannino1, and Rongmei Zhang, “Consequences of dichotomization,” 8 Pharmaceut. Statist. 50 (2009).

State-of-the-Art Legal Defenses and Shifty Paradigms

October 16th, 2021

The essence of a failure-to-warn claim is that (1) a manufacturer knows, or should know, about a harmful aspect of its product, (2) which knowledge is not appreciated by customers, (3) the manufacturer fails to warn adequately of this known harm, and (4) the manufacturer’s failure to warn causes the plaintiff to sustain the particular harm of which the manufacturer had knowledge, actual or constructive.

There are myriad problems with the assessing the knowledge component in failure-to-warn claims. Some formulations impute to manufacturers the knowledge of an expert in the field. First, which expert’s claim to knowledge counts for or against the existence of a duty? The typical formulation begs the question which expert’s understanding will control when experts in the field disagree. Second, and equally problematic, knowledge has a temporal aspect. There are causal relationships we “know” today, which we did not know in times past. This temporal component becomes even more refractory for failure-to-warn claims results when the epistemic criteria for claims of knowledge change over time.

In the early 20th century, infectious disease epidemiology, with its reliance upon Koch’s postulates. dominated the model of causation used in public and scientific discourse. The very nature of Koch’s postulates made the identification of a specific pathogen necessary to the causation of a specific disease. Later in the first half of the 20th century, epidemiologists and clinicians came to realize that the specific pathogen may be necessary but not sufficient for inducing a particular infectious disease. Still there was some comfort in having causal associations predicated upon necessary relationships. Clinicians and clinical scientists did not have to worry too much about probability theory or statistics.

The development of causal models in which the putative cause was neither necessary nor sufficient for bringing about the outcome of interest was a substantial shock to the system. In the absence of a one-to-one specificity, scientists had to account for confounding variables, in ways that they had not done so previously. The implications for legal state-of-the-art defenses could not be more profound. In the first half of the 20th century, case reports and series were frequently seen as adequate for suggesting and establishing causal relationships. By the end of the 1940s, scientists were well aware of the methodological inappropriateness of relying upon case reports and series, and the need for analytical epidemiologic studies to support causal claims.

Several historians of science have addressed the changing causal paradigm, which ultimately would permit and even encourage scientists to identify causal associations, even when the exposures studied were neither necessary nor sufficient to bring about the end point of interest. In 2011, Mark Parascandola, while he was an epidemiologist in the National Cancer Institute’s Tobacco Control Research Branch, wrote an important history of this paradigm shift and its implications in epidemiology.[1] His paper should be required reading for all lawyers who work on “long-tail” litigation, involving claims that risks were known to manufacturers even before World War II.

In Parascandola’s history, epidemiology and clinical science focused largely on infectious diseases in the early 20th century, and as a result, causal association was seen through the lens of Koch’s postulates with its implied model of necessary and sufficient conditions for causal attribution. Not until after World War II did “risk factor” epidemiology emerge to address the causal role of exposures – such as tobacco smoking – that were neither necessary nor sufficient for causing an outcome of interest.[2]

The shift from infectious to chronic diseases, such as cancer and cardiovascular disease, occurred in the 1950s, and brought with it, acceptance of a different concepts of causation, which involved stochastic events, indeterminism, multi-factorial contributions, and confounding of observations by independent but correlated causes. The causal criteria for infectious disease were generally unhelpful in supporting causal claims of chronic diseases.

Parascandola characterizes the paradigm shift as a “radical change,” influenced by developments in statistics, quantum mechanics, and causal theory.[3] Edward Cuyler Hammond, an epidemiologist with the American Cancer Society, for example, wrote in 1955, that:

“[t]he cause of an effect has sometimes been defined as a single factor which antecedes, which is necessary, and which is sufficient to produce the effect. Clearly this definition is inadequate for the study of biologic phenomena, which are produced by a complex pattern of environmental conditions interacting with the highly complex and variable make-up of living organisms.”[4]

The shift in causal models within epidemiologic thinking and research introduced new complexity with important practical implications. Gone was the one-to-one connection between pathogens (or pathogenic exposures) and specific diseases. Specificity was an important victim of the new model of causation. Causal models had to account for multi-factorial contributions to disease.[5] Confounding, the correlation between exposures of interest and other exposures that were truly driving the observations, became a substantial threat to validity. The discerning lens of analytical epidemiology was able to identify tobacco smoking as a cause of lung cancer only because of the large increased risks, ten-fold and greater, observed in multiple studies. There were no competing but independent risks of that magnitude, at hand, which could eliminate or reverse the observed tobacco risks.

Parascandola notes that in the 1950s, the criteria for causal assessment were in flux and the subject of debate:

“Previous informal rules or guides for inference, such as Koch’s postulates, were not adequate to identify partial causes of chronic disease based on a combination of epidemiologic and laboratory evidence.”[6]

As noted above, the legal implications of Parascandola’s historical analysis are hugely important.  Scientists and statisticians were scrambling to develop appropriate methodologies to accommodate the changed causal models and causal criteria. Mistakes were made along the way as the models and criteria changed. In Sir Richard Doll’s famous 1955 study of lung cancer among asbestos factory workers, the statistical methods were surprisingly primitive to modern epidemiology. Even more stunning was that Sir Richard failed to incorporate smoking histories and accounting for confounding from smoking before reaching a conclusion that lung cancer was associated with long-term asbestos factory work that had induced asbestosis.[7]

Not until the lae 1950s and early 1960s did statisticians develop multivariate models to help assess potential confounding.[8] Perhaps the most cited paper in epidemiology was published by Nathan Mantel (the pride of the Brooklyn Hebrew Orphan Asylum) and William Haenszel in 1959. Its approach to stratification of sample analyses was further elaborated upon by the authors and others all through the 1960s and into the 1970s.[9]

Similarly, the evolution of criteria for causal attribution based upon risk factor epidemiology required decades of discussion and debate. Reasonably well defined criteria did not emerge until the mid-1960s, with the famous Public Health Service report on smoking and lung cancer,[10] and Sir Austin Bradford Hill’s famous after-dinner talk to the Royal Society of Medicine.[11]

Several years before Parascandola published his historical analysis, three historians of science published a paper with a very similar thesis.[12] These authors noted that there was, indeed, a legitimate controversy over whether tobacco smoking caused lung cancer, in the 1950s early 1960s, as the mechanistic Koch’s postulates gave way to the statistical methods of risk-factor epidemiology. The historians’ paper observed that by the 1950s, infectious diseases such as tuberculosis were in retreat, and the public health community’s focus was on chronic diseases such as lung cancer. The lung cancer controversy of the 1950s pushed scientists to revise their conceptions of causation ,[13] and ultimately led to the strengthening of, and legitimizing, the field of epidemiology.[14] The growing acceptance of epidemiologic methods for identifying causes, neither necessary nor sufficient, pushed aside the attachment to Koch’s postulates and the skepticism over statistical reasoning.

Interestingly, this historians’ paper was funded completely by the Rollins Public Health of Emory University. Two of the authors had been sought out by a recruiting agency for the tobacco industry, but fell out with the agency and the tobacco companies when they realized that they could not support the litigation goals. In a footnote, the authors emphasized that their factual analysis and argument contradicted the industry’s desired defense.[15]

Reaching back even farther in time, there is the redoubtable Irving John Selikoff, who wrote in 1991:

“We are inevitably bound by the knowledge of the time in which we live. An example may be given. During the 1930s and 194Os, random instances of lung cancer occurring among workers exposed to asbestos were reported and attention was called to these by the collection of cases both in registers and in review papers. With the continued growth of the asbestos industry, it was deemed wise to epidemiologically examine the proposed association. This was done in an elegant, innovative, well-considered study by Richard Doll, a study which any one of us would have been proud to report in 1955.”[16]

What is ironic is that Dr. Selikoff had testified for plaintiffs’ counsel as an expert witness specifically on state of the art, or the question of when defendants should have known and warned that asbestos caused lung cancer.[17] Dr. Selikoff ultimately withdrew from testifying, in large part because his views on this matter were not particularly helpful to plaintiffs.

The shift in causal criteria, and rejection of case reports and case series, can be seen in the suggestion, in the 1930s, of a few pathologists who contended that silicosis caused lung cancer. The few scientists who made this causal claim relied upon heavily upon anecdotal and uncontrolled necropsy series.[18]

After World War II, these causal claims fell into disrepute as not properly supported by valid scientific methodology. Dr. Madge Thurlow Macklin, a female pioneer in clinical medicine and epidemiology,[19] and one the early adopters of statistical methodology in her work, debunked the causal claims:

“If silicosis is being considered as a causative agent in lung cancer, the control group should be as nearly like the experimental or observed group as possible in sex, age distribution, race, facilities for diagnosis, other possible carcinogenic factors, etc. The only point in which the control group should differ in an ideal study would be that they were not exposed to free silica, whereas the experimental group was. The incidence of lung cancer could then be compared in the two groups of patients.

This necessity is often ignored; and a ‘random’ control group is obtained for comparison on the assumption that any group taken at random is a good group for comparison. Fallacious results based on such studies are discussed briefly.”[20]

Macklin’s advice sounds like standard-operating procedure today, but in the 1940s, it was viewed as radical and wrong by many physicians and clinical scientists.

Of course, the change over time in the knowledge of, and techniques for, diagnostic methods, quantitative measurements, and disease definitions also affect litigated issues. The change in epistemic standards and causal criteria, however, fundamentally changed legal standards for tort liability. The shift from deterministic models of necessary and sufficient causation to risk factor causation had, and continues to have, enormous ramifications for the legal adjudication of questions concerning when companies, held to the knowledge of an expert in the field, should have started to warn about the risks created by their products. Mind the gap!


[1] Mark Parascandola, “The epidemiologic transition and changing concepts of causation and causal inference,” 64 Revue d’histoire des sciences 243 (2011).

[2] Id. at 245.

[3] Id. at 248.

[4] Id. at 252, citing Edward Cuyler Hammond, “Cause and Effect,” in Ernest L. Wynder, ed., The Biologic Effects of Tobacco (1955).

[5] Id. at 257.

[6] Id.

[7] Richard Doll, “Mortality from Lung Cancer in Asbestos Workers,” 12 Brit. J. Indus. Med. 81 (1955).

[8] See Parascandola at 258.

[9] Nathan Mantel & William Haenszel, “Statistical aspects of the analysis of data from retrospective studies of disease,” 22 J. Nat’l Cancer Instit. 19 (1959). See Mervyn Susser, “Epidemiology in the United States after World War II: The Evolution of Technique,” 7 Epidemiology Reviews 147 (1985).

[10] Surgeon General, Smoking and health : Report of the Advisory Committee to the surgeon general of the Public Health Service, PHS publication No. 1103 (1964).

[11] Austin Bradford Hill, “The Environment and Disease: Association or Causation?” 58 Proc. Royal Soc’y Med. 295, 295 (1965).

[12] Colin Talley, Howard I. Kushner & Claire E. Sterk, “Lung Cancer, Chronic Disease Epidemiology, and Medicine, 1948-1964,” 59 J. History Med. & Allied Sciences 329 (2004) [Talley]. Parascandola appeared not to have been aware of this article; at least he did not cite it.

[13] Id. at 374.

[14] Id. at 334.

[15] Id. at 329.

[16] Irving John Selikoff, “Statistical Compassion,” 44 J. Clin. Epidemiol. 141S, 142S (1991) (internal citations omitted) (emphasis added).

[17]Selikoff and the Mystery of the Disappearing Testimony,” (Dec. 3, 2010). See also Peter W.J. Bartrip, “Irving John Selikoff and the Strange Case of the Missing Medical Degrees,” 58 J. History Med. 3, 27 & n.88-92 (2003) (quoting insulator union President Andrew Haas, as saying “[w]e all owe a great debt of thanks for often and expert testimony on behalf of our members … .” Andrew Haas, Comments from the General President, 18 Asbestos Worker (Nov. 1972)).

[18] See, e.g., Max O. Klotz, “The Association Silicosis & Carcinoma of Lung 1939,” 35 Cancer Research 38 (1939); C.S. Anderson & J. Heney Dible, “Silicosis and carcinoma of the lung,” 38 J. Hygiene 185 (1938).

[19] Barry Mehler, “Madge Thurlow Macklin,” from Barbara Sicherman and Carl Hurd Green, eds., Notable American Women: The Modern Period 451-52 (1980); Laura Lynn WindsorWomen in Medicine: An Encyclopedia 134 (2002).

[20] Madge Thurlow Macklin, “Pitfalls in Dealing with Cancer Statistics, Especially as Related to Cancer of the Lung,” 14 Diseases Chest 525 532-33, 529-30 (1948). See alsoHistory of Silica Litigation – the Lung Cancer Angle,” (Feb. 3, 2019); “The Unreasonable Success of Asbestos Litigation,” (July 25, 2015); “Careless Scholarship about Silica History,” (July 21, 2014) (discussing David Egilman); “Silicosis, Lung Cancer, and Evidence-Based Medicine in North America,” (July 4, 2014).

Finding Big Blue

July 26th, 2021

The Washington Supreme Court recently upheld an $81.5 million verdict, against GPC and NAPA, in an asbestos peritoneal mesothelioma case. The award included $30 million for loss of consortium. Coogan v. Borg-Warner Morse Tec Inc., 12 Wash. App. 2d 1021, 2020 WL 824192 (2020), rev’d in part, No. 98296-1, 2021 Wash. LEXIS 383 *, 2021 WL 2835358 (Wash. July 8, 2021).[1] The main points of contention on appeal were plaintiffs’ counsel’s misconduct and the excessiveness of the verdict, which was for only compensatory damages. Twelve defendants settled before trial for a total of $4.4 million. Of the settling defendants, Defendant Manville paid $1.5 million.

Plaintiffs’ proofs against GPC and NAPA were for chrysotile exposure from their brake and clutch parts used by Coogan. Not surprisingly, given that Coogan died of peritoneal mesothelioma, there was a strong suspicion of crocidolite exposure from Manville’s transite product over the course of two years.  Apparently, GPC and NAPA failed to show that Coogan was exposed to crocidolite, even though the workplace was small and other workers had succumbed to asbestos disease.

While the court’s opinion on misconduct and the excessiveness of the verdict are of interest, the most interesting part of the story is what was not told. It is hard to imagine that defense counsel did not try hard to establish the workplace exposures to Manville’s transite. What is not clear is why they failed. Obviously, Manville took the threat seriously enough to pay a significant sum to settle the case before trial. Why could GPC and NAPA not prove at trial what Manville knew?  Were GPC and NAPA the victims of budgetary pressures or limited resources, or were they misled or stonewalled by plaintiffs’ counsel or co-workers?

Given the propensity for crocidolite, such as was used in Manville’s transite, to cause mesothelioma, and especially peritoneal mesothelioma, the trial defendants certainly had an adequate motivation to investigate and to document the crocidolite exposure. 

A recent, large, long-term cohort study in Denmark showed that vehicle mechanics, who use brake linings and clutch parts, as did Coogan, have no increased risk of mesothelioma. Compared with other workers, automobile mechanics actually had a lower than expect risk of mesothelioma or pleural cancer, with an age-adjusted hazard ratio of HR=0.74 (95% CI 0.55 to 0.99)), based upon 47 cases.[2]

The Danish study is in accord with previous studies and meta-analyses,[3] and stands in stark contrast with the epidemiology of mesothelioma among men and women exposed to crocidolite. By way of example, in a cohort of British workers who assembled gas masks during World War II, close to 9% of all deaths were due to mesothelioma.[4] In a published cohort study of workers at Hollingsworth & Vose, a company that made the filters for the Kent cigarette, close to 18 percent of all deaths were due to mesothelioma.[5]

Dr. Irving Selikoff and his colleagues worked assiduously to obscure the vast potency difference between chrysotile and crocidolite, by arguing falsely that crocidolite was not used in the United States,[6] and by suppressing their own research into disease at the Johns-Manville plant that manufactured transite and other products. What is interesting about the Coogan case is what has not been reported. Crocidolite is clearly the most potent cause of mesothelioma.[7] Even if chrysotile were to have posed a risk to someone such as Mr. Coogan, crocidolite exposure, even for just two years, likely represented multiple orders of magnitude greater risk for peritoneal mesothelioma. Without evidence that Coogan was exposed to crocidolite from Mansville’s transite, the manufacturers of brake and clutch parts were unable to seek an apportionment between exposures from their chrysotile and Mansville’s crocidolite. Trying the so-called chrysotile defense is more difficult without being able to show substantial amphibole asbestos exposure.  The bar, both plaintiffs’ and defendants’, could learn a great deal from what efforts were made to establish the crocidolite exposure, why they were unsuccessful, and how the efforts might go better in the future.


[1] Kirk Hartley kindly called my attention to this interesting case.

[2] Reimar Wernich Thomsen, Anders Hammerich Riis, Esben Meulengracht Flachs, David H Garabrant, Jens Peter Ellekilde Bonde, and Henrik Toft Sørensen, “Risk of asbestosis, mesothelioma, other lung disease or death among motor vehicle mechanics: a 45-year Danish cohort study,” Thorax (July 8, 2021), online ahead of print at <doi: 10.1136/thoraxjnl-2020-215041>.

[3] David H. Garabrant, Dominik D. Alexander, Paula E. Miller, Jon P. Fryzek, Paolo Boffetta, M. Jane Teta, Patrick A. Hessel, Valerie A. Craven, Michael A. Kelsh, and Michael Goodman, “Mesothelioma among Motor Vehicle Mechanics: An Updated Review and Meta-analysis,” 60 Ann. Occup. Hyg. 8 (2016); Michael Goodman, M. Jane Teta, Patrick A. Hessel, David H. Garabrant, Valerie A. Craven, Carolyn G. Scrafford, and Michael A. Kelsh, “Mesothelioma and lung cancer among motor vehicle mechanics: a meta-analysis,” 48 Ann. Occup. Hyg. 309 (2004).

[4] See J. Corbett McDonald, J. M. Harris, and Geoffry Berry, “Sixty years on: the price of assembling military gas masks in 1940,” 63 Occup. & Envt’l Med. 852 (2006). 

[5] James A. Talcott, Wendy A. Thurber, Arlene F. Kantor, Edward A. Gaensler, Jane F. Danahy, Karen H. Antman, and Frederick P. Li, “Asbestos-Associated Diseases in a Cohort of Cigarette-Filter Workers,” 321 New Engl. J. Med. 1220 (1989).

[6]Selikoff and the Mystery of the Disappearing Amphiboles” (Dec. 10, 2010); “Playing Hide the Substantial Factors in Asbestos Litigation” (Sept. 27, 2011).

[7] See, e.g., John T. Hodgson & Andrew A. Darnton, “The quantitative risks of mesothelioma and lung cancer in relation to asbestos exposure,” 14 Ann. Occup. Hygiene 565 (2000); Misty J Hein, Leslie T Stayner, Everett Lehman & John M Dement, “Follow-up study of chrysotile textile workers: cohort mortality and exposure-response,” 64 Occup. & Envt’l Med. 616 (2007); David H. Garabrant & Susan T. Pastula, “A comparison of asbestos fiber potency and elongate mineral particle (EMP) potency for mesothelioma in humans,” 361 Toxicology & Applied Pharmacol. 127 (2018) (“relative potency of chrysotile:amosite:crocidolite was 1:83:376”). See also D. Wayne Berman & Kenny S. Crump, “Update of Potency Factors for Asbestos-Related Lung Cancer and Mesothelioma,” 38(S1) Critical Reviews in Toxicology 1 (2008).

Judge Jack B. Weinstein – A Remembrance

June 17th, 2021

There is one less force of nature in the universe. Judge Jack Bertrand Weinstein died earlier this week, about two months shy of a century.[1] His passing has been noticed by the media, lawyers, and legal scholars[2]. In its obituary, the New York Times noted that Weinstein was known for his “bold jurisprudence and his outsize personality,” and that he was “revered, feared, and disparaged.” The obituary quoted Professor Peter H. Schuck, who observed that Weinstein was “something of a benevolent despot.”

As an advocate, I found Judge Weinstein to be anything but fearsome. His jurisprudence was often driven by intellectual humility rather than boldness or despotism. One area in which Judge Weinstein was diffident and restrained was in his exercise of gatekeeping of expert witness opinion. He, and his friend, the late Professor Margaret Berger, were opponents of giving trial judges discretion to exclude expert witness opinions on ground of validity and reliability. Their antagonism to gatekeeping was, no doubt, partly due to their sympathies for injured plaintiffs and their realization that plaintiffs’ expert witnesses often come up with dodgy scientific opinions to advance plaintiffs’ claims. In part, however, Judge Weinstein’s antagonism was due to his skepticism about judicial competence and his own intellectual humility.

Although epistemically humble, Judge Weinstein was not incurious. His interest in scientific issues occasionally got him into trouble, as when he was beguiled by Dr. Irving Selikoff and colleagues, who misled him on aspects of the occupational medicine of asbestos exposure. In 1990, Judge Weinstein issued a curious mea culpa. Because of a trial in progress, Judge Weinstein, along with state judge (Justice Helen Freedman), attended an ex parte private luncheon meeting with Dr. Selikoff. Here is how Judge Weinstein described the event:

“But what I did may have been even worse [than Judge Kelly’s conduct that led to his disqualification]. A state judge and I were attempting to settle large numbers of asbestos cases. We had a private meeting with Dr. Irwin [sic] J. Selikoff at his hospital office to discuss the nature of his research. He had never testified and would never testify. Nevertheless, I now think that it was a mistake not to have informed all counsel in advance and, perhaps, to have had a court reporter present and to have put that meeting on the record.”[3]

Judge Weinstein’s point about Selikoff’s having never testified was demonstrably false, but I impute no scienter for false statements to the judge. The misrepresentation almost certainly originated with Selikoff. Dr. Selikoff had testified frequently up to the point at which he and plaintiffs’ counsel realized that his shaky credentials and his pronouncements on “state of the art,” were hurtful to the plaintiffs’ cause. Even if Selikoff had not been an accomplished testifier, any disinterested observer should, by 1990, have known that Selikoff was himself not a disinterested actor in medical asbestos controversies.[4] The meeting with Selikoff apparently weighed on Judge Weinstein’s conscience. He repeated his mea culpa almost verbatim, along with the false statement about Selikoff’s never having testified, in a law review article in 1994, and then incorporated the misrepresentation into a full-length book.[5]

In his famous handling of the Agent Orange class action, Judge Weinstein manipulated the defendants into settling, and only then applied his considerable analytical ability in dissecting the inadequacies of the plaintiffs’ causation case. Rather than place the weight of his decision on Rule 702, Judge Weinstein dismembered the causation claim by finding that the bulk of what the plaintiffs’ expert witnesses relied upon under Rule 703 was unreasonable. He then found that what remained, if anything, could not reasonably support a verdict for plaintiffs, and he entered summary judgment for the defense in the opt-out cases.[6]

In 1993, the U.S. Supreme Court breathed fresh life into the trial court’s power and obligation to review expert witness opinions and to exclude unsound opinions.[7] Several months before the Supreme Court charted this new direction on expert witness testimony, the silicone breast implant litigation, fueled by iffy science and iffier scientists, erupted.[8] In October 1994, the Judicial Panel on Multi-District Litigation created MDL 926, which consolidated the federal breast implant cases before Judge Sam Pointer, in the Northern District of Alabama. Unlike most contemporary MDL judges, however, Judge Pointer did not believe that Rule 702 and 703 objections should be addressed by the MDL judge. Pointer believed strongly that the trial judges, in the individual, remanded cases, should rule on objections to the validity of proffered expert witness opinion testimony. As a result, so-called Daubert hearings began taking place in district courts around the country, in parallel with other centralized proceedings in MDL 926.

By the summer of 1996, Judge Robert E. Jones had a full-blown Rule 702 attack on the plaintiffs’ expert witnesses before him, in a case remanded from MDL 926. In the face of the plaintiffs’ MDL leadership committee’s determined opposition, Judge Jones appointed four independent scientists to serve as scientific advisors. With their help, in December 1996, Judge Jones issued one of the seminal rulings in the breast implant litigation, and excluded the plaintiffs’ expert witnesses.[9]

While Judge Jones was studying the record, and writing his opinion in the Hall case, Judge Weinstein, with a judge from the Southern District of New York, conducted a two-week Rule 702 hearing, in his Brooklyn courtroom. Judge Weinstein announced at the outset that he had studied the record from the Hall case, and that he would incorporate it into his record for the cases remanded to the Southern and Eastern Districts of New York.

I had one of the first witnesses, Dr. Donnard Dwyer, before Judge Weinstein during that chilly autumn of 1996. Dwyer was a very earnest immunologist, who appeared on direct examination to endorse the methodological findings of the plaintiffs’ expert witnesses, including a very dodgy study by Dr. Douglas Shanklin. On cross-examination, I elicited Dwyer’s view that the Shanklin study involved fraudulent methodology and that he, Dwyer, would never use such a method or allow a graduate student to use it. This examination, of course, was great fun, and as I dug deeper with relish, Judge Weinstein stopped me, and asked rhetorically to the plaintiffs’ counsel, whether any of them intended to rely upon the discredited Shanklin study. My main adversary Mike Williams did not miss a beat; he jumped to his feet to say no, and that he did not know why I was belaboring this study. But then Denise Dunleavy, of Weitz & Luxenberg, knowing that Shanklin was her listed expert witness in many cases, rose to say that her expert witnesses would rely upon the Shanklin study. Incredulous, Weinstein looked at me, rolled his eyes, paused dramatically, and then waved his hand at me to continue.

Later in my cross-examination, I was inquiring about another study that reported a statistic from a small sample. The authors reported a confidence interval that included negative values for a test that could not have had any result less than zero. The sample was obviously skewed, and the authors had probably used an inappropriate parametric test, but Dwyer was about to commit to the invalidity of the study when Judge Weinstein stopped me. He was well aware that the normal approximation had created the aberrant result, and that perhaps the authors only sin was in failing to use a non-parametric test. I have not had many trial judges interfere so knowledgably.

In short order, on October 23, 1996, Judge Weinstein issued a short, published opinion, in which he ducked the pending Rule 702 motions, and he granted partial summary judgment on the claims of systemic disease.[10] Only the lawyers involved in the matters would have known that there was no pending motion for summary judgment!

Following up with grant of summary judgment, Judge Weinstein appointed a group of scientists and a legal scholar, to help him assemble a panel of Rule 706 expert witnesses for future remanded case. Law Professor Margaret Berger, along with Drs. Joel Cohen and Alan Wolff, began meeting with the lawyers to identify areas of expertise needed by the court, and what the process of court-appointment of neutral expert witnesses would look like.

The plaintiffs’ counsel were apoplectic. They argued to Judge Weinstein that Judge Pointer, in the MDL, should be supervising the process of assembling court-appointed experts. Of course, the plaintiffs’ lawyers knew that Judge Pointer, unlike Judges Jones and Weinstein, believed that both sides’ expert witnesses were extreme, and mistakenly believed that the truth lay between. Judge Pointer was an even bigger foe of gatekeeping, and he was generally blind to the invalid evidence put forward by plaintiffs. In response to the plaintiffs’ counsel’s, Judge Weinstein sardonically observed that if there were a real MDL judge, he should take it over.

Within a month or so, Judge Pointer did, in fact, take over the court-appointed expert witness process, and incorporated Judge Weinstein’s selection panel. The process did not going very smoothly in front of the MDL judge, who allowed the plaintiffs lawyers to slow down the process by throwing in irrelevant documents and deploying rhetorical tricks. The court-appointed expert witnesses did not take kindly to the shenanigans, or to the bogus evidence. The expert panel’s unanimous rejection of the plaintiffs’ claims of connective tissue disease causation was an expensive, but long overdue judgment from which there was no appeal. Not many commentators, however, know that the panel would never have happened but for Judge Weinstein’s clever judicial politics.

In April 1997, while Judge Pointer was getting started with the neutral expert selection panel,[11] the parties met with Judge Weinstein one last time to argue the defense motions to exclude the plaintiffs’ expert witnesses. Invoking the pendency of the Rule 706 court-appointed expert witness processs in the MDL, Judge Weinstein quickly made his view clear that he would not rule on the motions. His Honor also made clear that if we pressed for a ruling, he would deny our motions, even though he had also ruled that plaintiffs’ could not make out a submissible case on causation.

I recall still the frustration that we, the defense counsel, felt that April day, when Judge Weinstein tried to explain why he would grant partial summary judgment but not rule on our motions contra plaintiffs’ expert witnesses. It would be many years later, before he let his judicial assessment see the light of day. Two decades and then some later, in a law review article, Judge Weinstein made clear that “[t]he breast implant litigation was largely based on a litigation fraud. …  Claims—supported by medical charlatans—that enormous damages to women’s systems resulted could not be supported.”[12] Indeed.

Judge Weinstein was incredibly smart and diligent, but he was human with human biases and human fallibilities. If he was a despot, he was at least kind and benevolent. In my experience, he was always polite to counsel and accommodating. Appearing before Judge Weinstein was a pleasure and an education.


[1] Laura Mansnerus, “Jack B. Weinstein, U.S. Judge With an Activist Streak, Is Dead at 99,” N.Y. Times (June 15, 2021).

[2] Christopher J. Robinette, “Judge Jack Weinstein 1921-2021,” TortsProf Blog (June 15, 2021).

[3] Jack B. Weinstein, “Learning, Speaking, and Acting: What Are the Limits for Judges?” 77 Judicature 322, 326 (May-June 1994).

[4]Selikoff Timeline & Asbestos Litigation History” (Dec. 20, 2018).

[5] See Jack B. Weinstein, “Limits on Judges’ Learning, Speaking and Acting – Part I- Tentative First Thoughts: How May Judges Learn?” 36 Ariz. L. Rev. 539, 560 (1994) (“He [Selikoff] had never testified and would never testify.”); Jack B. Weinstein, Individual Justice in Mass Tort Litigation: The Effect of Class Actions, Consolidations, and other Multi-Party Devices 117 (1995) (“A court should not coerce independent eminent scientists, such as the late Dr. Irving Selikoff, to testify if, like he, they prefer to publish their results only in scientific journals.”)

[6] In re Agent Orange Product Liab. Litig., 597 F. Supp. 740, 785 (E.D.N.Y. 1984), aff’d 818 F.2d 145, 150-51 (2d Cir. 1987)(approving district court’s analysis), cert. denied sub nom. Pinkney v. Dow Chemical Co., 487 U.S. 1234 (1988);  In re “Agent Orange” Prod. Liab. Litig., 611 F. Supp. 1223 (E.D.N.Y. 1985), aff’d, 818 F.2d 187 (2d Cir. 1987), cert. denied, 487 U.S. 1234 (1988).

[7] Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993).

[8] Reuters, “Record $25 Million Awarded In Silicone-Gel Implants Case,” N.Y. Times (Dec. 24, 1992).

[9] See Hall v. Baxter Healthcare Corp., 947 F. Supp. 1387 (D. Ore. 1996).

[10] In re Breast Implant Cases, 942 F. Supp. 958 (E.& S.D.N.Y. 1996).

[11] MDL 926 Order 31 (May 31, 1996) (order to show cause why a national Science Panel should not be appointed under Federal Rule of Evidence 706); MDL 926 Order No. 31C (Aug. 23, 1996) (appointing Drs. Barbara S. Hulka, Peter Tugwell, and Betty A. Diamond); Order No. 31D (Sept. 17, 1996) (appointing Dr. Nancy I. Kerkvliet).

[12] Hon. Jack B. Weinstein, “Preliminary Reflections on Administration of Complex Litigation” 2009 Cardozo L. Rev. de novo 1, 14 (2009) (emphasis added).

NJ Appellate Division Calls for Do Over in Baby Powder Dust Up

May 22nd, 2021

There was quite a bit of popular media reporting of the $117 million (compensatory and punitive damages) awarded by a Middlesex County, New Jersey, jury to a man who claimed his mesothelioma had been caused by his use of baby powder. There was much less media coverage last month of the New Jersey Appellate Division’s reversal of the underlying verdicts, on grounds that the trial Judge Ana C. Viscomi had abused her discretion on several key issues.[1] The New Jersey appellate court reversed the trial court’s judgment, and remanded the Lanzo case for a new trial, in a carefully reasoned decision.[2]

Johnson & Johnson Consumer Inc. (JJCI) and Imerys Talc America, Inc. (Imerys) appealed from the judgment entered by Judge Viscomi, on April 23, 2018. The appellants lodged several points of error, but the most erroneous of the erroneous trial court decisions seemed to involve a laissez-faire attitude to weak and unreliable proffered expert witness opinions.

Judge Viscomi conducted a Rule 104 hearing on the admissibility of testing of plaintiffs’ expert witness, William Longo, on crowd-sourced samples of baby powder, without chain of custody or provenance evidence. Judge Viscomi denied the challenge to Longo’s test results.

The defense had also filed Rule 702 challenges to plaintiffs’ expert witnesses, James S. Webber, Ph.D., and Jacqueline Moline, M.D., and their opinion that non-asbestiform amphibole cleavage fragments can cause mesothelioma. Judge Viscomi refused these pre-trial motions, and refused to conduct a pre-trial Rule 104 hearing on the proffered opinions. Her Honor’s denial of the Rule 702 was accompanied with little to no reasoning, which proved to be the determinant of her abuse of discretion, and deviation from the standard of judicial care.

At trial, the defense re-asserted its objections to Moline’s opinion on cleavage fragments, but Judge Viscomi permitted Moline to testify about “non-asbestiform cleavage fragments from a medical point of view.” In other words, the trial judge gave Dr. Moline carte blanche to address causation.

Understandably, on appeal, JJCI and Imerys assigned various errors. With respect to the scientific evidence, the defendants alleged that plaintiffs’ expert witnesses (Webber and Moline) failed to:

“(1) explain what causes the human body to respond in the same way to the different mineral forms;

(2) acknowledge the contrary opinions of scientists and government agencies;

(3) provide evidentiary support for their opinion that non-asbestiform minerals can cause mesothelioma; and

(4) produce evidence that their theory that non-asbestiform minerals are harmful had been subject to peer-review and publication or was generally accepted in the scientific community.”

The Federal Fiber

The genesis of the scientific dispute lay in the evolution of the definition of asbestos itself. Historically, asbestos was an industrial term for one of six different minerals, the serpentine mineral chrysotile, and the amphibole minerals, amosite, crocidolite, tremolite, anthophyllite, and actinolite. Chrysotile is, by mineralogical definition, a serpentine mineral in fibrous form.  If not fibrous, the mineral is typically called antigorite.

For the five amphiboles, the definitional morass deepens. Amosite is, again, an industrial term, an acronym for “asbestos mines of South Africa,” although South Africa once mined chrysotile and crocidolite as well.  Amosite is an iron-rich amphibole in the cummingtonite-grunerite family, with a fibrous habit.  Cummingtonite-grunerite can be either fibrous or non-fibrous in mineral habit.

Crocidolite is an amphibole that by definition is fibrous. The same mineral, if not fibrous, is known as riebeckite. Crocidolite is, by far, the most potent cause of mesothelioma.

The remaining amphiboles, tremolite, anthophyllite, and actinolite, have the same mineralogical designation, regardless whether they occur as fibers or in non-fibrous forms.

The designation of a mineral as “asbestiform” is also rather vague, apparently conveying an industrial functionality from its fibrosity. Medically, the term asbestiform became associated with minerals that have sufficiently high aspect ratio, and small cross-sectional diameter, to be considered potentially capable of inducing pulmonary fibrosis or mesothelioma.

In 1992, the federal OSHA regulations removed non-asbestiform actinolite, tremolite, and anthophyllite from the safety standard, based upon substantial evidence that the non-asbestiform occurrences of these minerals did not present the health risks associated with asbestiform amphiboles. Because nothing is ever simple, the National Institute for Occupational Safety and Health (NIOSH) persisted in its recommendation that OSHA continue to regulate non-asbestiform amphiboles under asbestos regulatory standards. This NIOSH pronouncement, however, was extremely controversial among the ranks of NIOSH scientists. In any event, NIOSH recommendations are just that, suggestions and not binding regulations.

The mineralogical, medical, and regulatory definitions of asbestos and asbestiform minerals vary greatly, and require a great deal of discipline and precision in discussing what causes mesothelioma. The health effects of non-asbestiform minerals have been studied, however, and generally shown not to cause mesothelioma.[3]

Judge Viscomi Abused Her Discretion

The Appellate Division panel applied Accutane’s abuse of discretion standard, which permits judges to screw up to some extent, but requires reversal for their mistakes when “so wide off the mark that a manifest denial of justice resulted.” The appellate court had little difficulty in saying that the trial court was “so wide off the mark” in addressing expert witness opinion admissibility.

James Webber

In the Lanzo case, plaintiffs’ expert witnesses, James Webber and Jacqueline Moline, both opined that non-asbestiform minerals can cause mesothelioma. The gravamen of the defense’s appeal was that these expert witnesses had failed to support their opinions and that the trial judge had misapplied the established judicial gatekeeping procedures required by the New Jersey Supreme Court, in In re Accutane Litigation, 234 N.J. 340 (2018).

The Appellate Division then set out to do what Judge Viscomi had failed to do – look at the proffered opinions and assess whether they followed reasonably and reliably from the expert witnesses’ stated grounds. Although Webber opined that cleavage fragments could cause mesothelioma, he had never studied the issue himself; nor was he aware of any studies showing that showed that non-asbestiform cleavage fragments can cause mesothelioma. Webber had never expressed his opinion in scientific publications, and he failed to cite any support for his opinion in his report.

At trial, Judge Viscomi permitted Webber to go beyond his anemic report and to cite reliance upon four sources for his opinion. The Appellate Division carefully reviewed each of the four sources, and found that they either did not support Webber’s opinions or they were as equally without evidentiary support. “Webber did not identify any data underlying his opinion. Further, he did not demonstrate that any of the authorities he relied on would be reasonably relied on by other experts in his field to reach an opinion regarding causation.”

Webber cited an article by Victor Roggli, who opined that he had found asbestiform and non-asbestiform fibers in the lungs of mesothelioma patients, but who went on to conclude that fibers were the likely cause. Webber also cited an article by NIOSH scientist Martin Harper, who stated the opinion, without evidentiary support that NIOSH did not believe, in 2008, that there was “sufficient evidence for a different toxicity for non-asbestiform amphibole particles that meet the morphological criteria for a fiber.”[4]

Although Harper and company appeared to be speaking on behalf of NIOSH, in 2011, the agency clarified its position to state that its previous inclusion of non-asbestiform minerals in the definition of respirable asbestos fibers had been based upon “inclusive science”:

“Epidemiological evidence clearly indicates a causal relationship between exposure to fibers from the asbestos minerals and various adverse health outcomes, including asbestosis, lung cancer, and mesothelioma. However, NIOSH has viewed as inconclusive the results from epidemiological studies of workers exposed to EMPs[9] [elongate mineral particles] from the non[-]asbestiform analogs of the asbestos minerals.”[5]

The Appellate Division was equally unimpressed with Webber’s citation of a geologist who stated an opinion in 2009, that “using the term ‘asbestiform’ to differentiate a hazardous from a non-hazardous substance has no foundational basis in the medical sciences.” Not only was the geologist, Gregory P. Meeker, lacking in medical expertise, but his article was non-peer-reviewed (for what little good that would have done) and his opinion did not cite any foundational evidence or data in an appropriate scientific study.

Webber cited to an Environmental Protection Agency (EPA) document,[6] which stated that

“[f]or the purposes of public health assessment and protection, [the] EPA makes no distinction between fibers and cleavage fragments of comparable chemical composition, size, and shape.”

The Appellate Division observed that the EPA not provide any scientific support for its assessment. Furthermore, the language cited by Webber clearly suggests that the EPA was issuing a precautionary view, not a scientific one.

Considering the Daubert factors, and New Jersey precedent, the Appellate Division readily found that Webber’s opinion was inadmissible. His opinion about non-asbestiform minerals was unsupported by data and analysis in published, peer-reviewed studies; the opinion was clearly not generally accepted; and the opinion had never been published by Webber himself. Plaintiffs had failed to show that Webber’s “methodology involv[ed] data and information of the type reasonably relied on by experts in the scientific field.”[7] The trial court’s observation that the issue of cleavage fragments was “contested” could not substitute for the required assessment of methodology and of the underlying data relied upon by Webber. Judge Viscomi abused her discretion in admitting Webber’s testimony.

Jacqueline Moline

Moline’s expert testimony that non-asbestiform minerals can cause mesothelioma suffered from many of the same defects as Webber’s opinion on this topic. The trial court once again did not conduct a pre-trial or in-trial hearing to assess Moline’s opinion, and it did not perform the rigorous assessment required by Rule 702 and the Accutane case to determine whether Moline’s opinions met the applicable (so-called Daubert) standards. The Appellate Division emphatically held that the trial court erred in permitting Moline to testify, over objection.

Moline vacuously opined that non-asbestiform amphiboles cause mesothelioma, but failed to identify any specific studies that actually supported this proposition. Like Webber, she pointed to an EPA document, from 2006, which also failed to support her asseverations. Moline also claimed support from the CDC, the American Thoracic Society, and other EPA pronouncements, but never cited anything specifically. In her pre-trial report, Moline claimed that New York state talc minerals experienced mesotheliomas from exposure to the mining and milling of talc that contained about “50% non-asbestiform anthophyllite and tremolite.” Moline’s report, however, was devoid of any reference for this remarkable claim.

Moline’s trial testimony was embarrassed on cross-examination when the defense confronted her with prior testimony she gave in another case, in which she testified that she lacked “information … one way or the other” say whether non-asbestiform minerals were carcinogenic. Moline shrugged off the impeachment with a claim that she had since come to learn of mesothelioma occurrences among patients with non-asbestiform mineral exposures. Nonetheless, Moline still could not identify the studies she relied upon to answer the question whether “asbestos-related diseases can be caused by the non-asbestiform varieties of the six regulated forms of asbestos.”

Reversal and Remand

Having concluded that the trial court erred and abused its discretion in denying the defense motions contra Webber and Moline, and having found that the error was harmful to the defense’s right to a fair trial, the appellate court reversed and remanded for new (separate) trials against JJCI and Imerys. There will be, no doubt, attempts to persuade the New Jersey Supreme Court to consider the issues further. The state Supreme Court’s jurisdiction is discretionary, and assuming that the high Court rejects petitions for certification, the case will return to the Middlesex County trial court. The intended nature of further trial court proceedings is, at best, a muddle. The Appellate Division has already done what Judge Viscomi failed to do. The three-judge panel carefully reviewed the plaintiffs’ proffered opinion testimony on causation and found it inadmissible. It would thus seem that the order of business would be for the defense to file motions for summary judgment for lack of admissible causation opinions, and for the trial court to enter judgment for the defense.

————————————————————————————————————

[1] To be fair, there was some coverage in local, and in financial and legal media. See, e.g., Jef Feeley, “J&J Gets Banker’s $117 Million Talc Verdict Tossed on Appeal,” (April 28, 2021); Mike Deak, “Appeals court overturns $117 million Johnson & Johnson baby powder verdict,” My Central Jersey (April 28, 2021); “J&J, Imerys Beat $117M Talc Verdicts Over Flawed Testimony,” Law360 (April 28, 2021); Irvin Jackson, “$117M J&J Talc Cancer Verdict Overturned By New Jersey Appeals Court,” About Lawsuits (April 30, 2021).

[2] See Lanzo v. Cyprus Amax Minerals Co., Docket Nos. A-5711-17, A-5717-17, New Jersey Superior Court, App. Div. (April 28, 2021).

[3] SeeIngham v. Johnson & Johnson – A Case of Meretricious Mensuration?” (July 3, 2020); “ Tremolitic Tergiversation or Ex-PIRG-Gation?” (Aug. 11, 2018).

[4] “Differentiating Non-Asbestiform Amphibole and Amphibole Asbestos by Size Characteristics,” 5 J. Occup. & Envt’l Hygiene 761 (2008).

[5] NIOSH, “Asbestos Fibers and Other Elongate Mineral Particles: State of the Science and Roadmap for Research,” Current Intelligence Bulletin 62 (April 2011).

[6] The document in question was issued in 2006, by EPA Region 9, in response to a report prepared by R.J. Lee Group, Inc. The regional office of the EPA criticized the R.J. Lee report for applying “a [g]eologic [d]efinition rather than a [p]ublic [h]ealth [d]efinition to [c]haracterize [m]icroscopic [s]tructures,” noting that the EPA made “no distinction between fibers and cleavage fragments of comparable chemical composition, size, and shape.” This document thus did not address, with credible evidence, the key issue in the Lanzo case.

[7] Lanzo (quoting Rubanick, 125 N.J. at 449).