TORTINI

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

Archives »

Reference Manual – Desiderata for 4th Edition – Part III – Differential Etiology

February 1st, 2023

Admittedly, I am playing the role of the curmudgeon here by pointing out errors or confusions in the third edition of the Reference Manual.  To be sure, there are many helpful and insightful discussions throughout the Manual, but they do not need to be revised.  Presumably, the National Academies and the Federal Judicial Center are undertaking the project of producing a fourth edition because they understand that revisions, updates, and corrections are needed. Otherwise, why bother?

To be sure, there are aspects of the third edition’s epidemiology chapter that get some important points right. 

(1) The chapter at least acknowledges that small relative risks (1 < RR <3) may be insufficient to support causal inferences.[1]

(2) The chapter correctly notes that the method known as “differential etiology” addresses only specific causation, and that the method presupposes that general causation has been established.[2]

(3) The third edition correctly observes that clinicians generally are not concerned with etiology as much as with diagnosis of disease.[3] The authors of the epidemiology chapter correctly observe that “[f]or many health conditions, the cause of the disease or illness has no relevance to its treatment, and physicians, therefore, do not employ this term or pursue that question.”[4] This observation alone should help trial courts question whether many clinicians have even the pretense of expertise to offer expert causation opinions.[5]

(4) With respect to so-called differential etiology, the third edition correctly states that this mode of reasoning is a logically valid argument if premises are true; that is, general causation must be established for each “differential etiology.” The epidemiology chapter observes that “like any scientific methodology, [differential etiology] can be performed in an unreliable manner.”[6]

(5) The third edition reports that the differential etiology argument as applied in litigation is often invalid because not all the differentials other than the litigation claim have been ruled out.[7]

(6) The third edition properly notes that for diseases for which the causes are largely unknown, such as most birth defects, a differential etiology is of little benefit.[8] Unfortunately, the third edition offered no meaningful guidance for how courts should consider differential etiologies offered when idiopathic cases make up something less “than largely,” (0% < Idiopathic < 10%, 20%, 30%, 40, 50%, etc.).The chapter acknowledges that:

“Although differential etiologies are a sound methodology in principle, this approach is only valid if … a substantial proportion of competing causes are known. Thus, for diseases for which the causes are largely unknown, such as most birth defects, a differential etiology is of little benefit.”[9]

Accordingly, many cases reject proffered expert witness testimony on differential etiology, when the witnesses failed to rule out idiopathic causes in the case at issue. What is a substantial proportion?  Unfortunately, the third edition did not attempt to quantify or define “substantial.” The inability to rule out unknown etiologies remains the fatal flaw in much expert witness opinion testimony on specific causation.

Errant Opinions on Differential Etiology

The third edition’s treatment of differential etiology does leave room for improvement. One glaring error is the epidemiology chapter’s assertion that “differential etiology is a legal invention not used by physicians.”[10] Indeed, the third edition provides a definition for “differential etiology” that reinforces the error:

“differential etiology. Term used by the court or witnesses to establish or refute external causation for a plaintiff’s condition. For physicians, etiology refers to cause.”[11]

The third edition’s assertion about legal provenance and exclusivity can be quickly dispelled by a search on “differential etiology” in the National Library of Medicine’s PubMed database, which shows up dozens of results, going back to the early 1960s. Some citations are supplied in the notes.[12] A Google Ngram for “differential etiology” in American English shows prevalent usage well before any of the third edition’s cited cases:

The third edition’s erroneous assertion about the provenance of “differential etiology” has been echoed by other law professors. David Faigman, for instance, has claimed 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.”[13]

Faigman’s claim that courts and commentators have not defined the methodology underlying differential etiology is wrong. Just as hypothesis testing is predicated upon a probabilistic version of modus tollens, differential etiology is based upon “iterative disjunctive syllogism,” or modus tollendo ponens. Basic propositional logic recognizes that such syllogisms are valid arguments,[14] in which one of its premises is a disjunction (P v Q), and the other premise is the negation of one of the disjuncts:

P v Q

~P­­­_____

∴ Q

If we expand the disjunctive premise to more than one disjunction, we can repeat the inference (iteratively), eliminating one disjunct at a time, until we arrive at a conclusion that is a simple, affirmative proposition, without any disjunctions in it.

P v Q v R

~P­­­_____

∴ Q v R

     ~Q­­­_____

∴ R

Hence, the term “iterative disjunctive syllogism.” Sherlock Holmes’ fans, of course, will recognize that iterative disjunctive syllogism is nothing other than the process of elimination, as explained by the hero of Sir Arthur Conan Doyle’s short stories.[15]

The fourth edition should correct the error of the third edition, and it should dispel the strange notion that differential etiology is not used by scientists or clinicians themselves.

Supreme Nonsense on Differential Etiology

In 2011, the Supreme Court addressed differential etiology in a case, Matrixx Initiatives, in stunningly irrelevant and errant dicta. The third edition did not discuss this troublesome case, in which the defense improvidently moved to dismiss a class action complaint for securities violations allegedly arising from the failure to disclose multiple adverse event reports of anosmia from the use of the defendant’s product, Zicam. The basic reason for the motion on the pleadings was that the plaintiffs’ failed to allege a statistically significant and causally related increased risk of anosmia.  The Supreme Court made short work of the defense argument because material events, such as an FDA recall, did not require the existence of a causal relationship between Zicam use and anosmia. The defense complaints about statistical significance, causation, and their absence, were thus completely beside the point of the case.  Nonetheless, it became the Court’s turn for improvidence in addressing statistical and causation issues not properly before it. With respect to causation, the Court offered this by way of obiter dictum:

“We note that courts frequently permit expert testimony on causation based on evidence other than statistical significance. See, e.g., Best v. Lowe’s Home Centers, Inc., 563 F. 3d 171, 178 (6th Cir 2009); Westberry v. Gislaved Gummi AB, 178 F. 3d 257, 263–264 (4th Cir. 1999) (citing cases); Wells v. Ortho Pharmaceutical Corp., 788 F. 2d 741, 744–745 (11th Cir. 1986). We need not consider whether the expert testimony was properly admitted in those cases, and we do not attempt to define here what constitutes reliable evidence of causation.”[16]

This part of the Court’s opinion was stunningly wrong about the Court of Appeals’ decisions on statistical significance[17] and on causation. The Best and the Westberry decisions were both cases that turned on specific, not general, causation.  Statistical significance this was not part of the reasoning or rationale of the cited cases on specific caustion. Both cases assumed that general causation was established, and inquired into whether expert witnesses could reasonably and validly attribute the health outcome in the case to the exposures that were established causes of such outcomes.  The Court’s selection of these cases, quite irrelevant to its discussion, appears to have come from the Solicitor General’s amicus brief in Matrixx, but mindlessly adopted by the Court.

Although cited for an irrelevant proposition, the Supreme Court’s selection of the Best’s case was puzzling because the Sixth Circuit’s discussion of the issue is particularly muddled. Here is the relevant language from Best:

“[A] doctor’s differential diagnosis is reliable and admissible where the doctor

(1) objectively ascertains, to the extent possible, the nature of the patient’s injury…,

(2) ‘rules in’ one or more causes of the injury using a valid methodology,

and

(3) engages in ‘standard diagnostic techniques by which doctors normally rule out alternative causes” to reach a conclusion as to which cause is most likely’.”[18]

Of course, as the authors of the third edition’s epidemiology chapter correctly note, physicians rarely use this iterative process to arrive at causes of diseases in an individual; they use it to identify the disease or disease process that is responsible for the patient’s signs and symptoms.[19] The Best court’s description does not make sense in that it characterizes the process as ruling in “one or more” causes, and then ruling out alternative causes.  If an expert had ruled in only one cause, then there would be no need or opportunity to rule out an alternative cause.  If the one ruled-in cause was ruled out for other reasons, then the expert witness would be left with a case of idiopathic disease.

In any event, differential etiology was irrelevant to the general causation issue raised by the defense in Matrixx Initiatives. After the Supreme Court correctly recognized that causation was largely irrelevant to the securities fraud claim, it had no reason to opine on general causation.  Certainly, the Supreme Court had no reason to cite two cases on differential etiology in a case that did not even require allegations of general causation. The fourth edition of the Reference Manual should put Matrixx Initatives in its proper (and very limited) place.

[1] RMSE3d at 612 & n.193 (noting that “one commentator contends that, because epidemiology is sufficiently imprecise to accurately measure small increases in risk, in general, studies that find a relative risk less than 2.0 should not be sufficient for causation. The concern is not with specific causation but with general causation and the likelihood that an association less than 2.0 is noise rather than reflecting a true causal relationship. See Michael D. Green, “The Future of Proportional Liability,” in Exploring Tort Law (Stuart Madden ed., 2005); see also Samuel M. Lesko & Allen A. Mitchell, “The Use of Randomized Controlled Trials for Pharmacoepidemiology Studies,” in Pharmacoepidemiology 599, 601 (Brian Strom ed., 4th ed. 2005) (“it is advisable to use extreme caution in making causal inferences from small relative risks derived from observational studies”); Gary Taubes, “Epidemiology Faces Its Limits,” 269 Science 164 (1995) (explaining views of several epidemiologists about a threshold relative risk of 3.0 to seriously consider a causal relationship); N.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”); 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.”). For many other supporting comments and observations, see “Small Relative Risks and Causation” (June 28, 2022).

[2] RMSE3d. at 618 (“Although differential etiologies are a sound methodology in principle, this approach is only valid if general causation exists … .”). In the case of a novel putative cause, the case may give rise to a hypothesis that the putative cause can cause the outcome, in general, and did so in the specific case.  That hypothesis must, of course, then be tested and supported by appropriate analytical methods before it can be accepted for general causation and as a putative specific cause in a particular individual.

[3] RMSE3d at 617.

[4] RMSE3d at 617 & n. 211 (citing Zandi v. Wyeth, Inc., No. 27-CV-06-6744, 2007 WL 3224242 (D. Minn. Oct. 15, 2007) (observing that physicians do assess the cause of patients’ breast cancers)).

[5] See, e.g., Tamraz v. BOC Group Inc., No. 1:04-CV-18948, 2008 WL 2796726 (N.D.Ohio July 18, 2008)(denying Rule 702 challenge to treating physician’s causation opinion), rev’d sub nom. Tamraz v. Lincoln Elec. Co., 620 F.3d 665 (6th Cir. 2010)(carefully reviewing record of trial testimony of plaintiffs’ treating physician; reversing judgment for plaintiff based in substantial part upon treating physician’s speculative causal assessment created by plaintiffs’ counsel), cert. denied, ___ U.S. ___ , 131 S. Ct. 2454 (2011).

[6] RMSE3d at 617-18 & n. 215.

[7] See, e.g, Milward v. Acuity Specialty Products Group, Inc., Civil Action No. 07–11944–DPW, 2013 WL 4812425 (D. Mass. Sept. 6, 2013) (excluding plaintiffs’ expert witnesses on specific causation), aff’d sub nom., Milward v. Rust-Oleum Corp., 820 F.3d 469 (1st Cir. 2016). Interestingly, the earlier appellate journey taken by the Milward litigants resulted in a reversal of a Rule 702 exclusion of plaintiff’s general causation expert witnesses. That reversal meant that there was no longer a final judgment.  The exclusion of specific causation witnesses was affirmed by the First Circuit, and the general causation opinion was no longer necessary to the final judgment. See “Differential Diagnosis in Milward v. Acuity Specialty Products Group” (Sept. 26, 2013); “Differential Etiology and Other Courtroom Magic” (June 23, 2014).

[8] RMSE3d at 617-18 & n. 214.

[9] See RMSE at 618 (internal citations omitted).

[10] RMSE3d at 691 (emphasis added).

[11] RMSE3d at 743.

[12] See, e.g., 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).

[13] 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, 439, 444 (2015). See also “David Faigman’s Critique of G2i Inferences at Weinstein Symposium” (Sept. 25, 2015).

[14] See Irving Copi & Carl Cohen Introduction to Logic at 362 (2005).

[15] See, e.g., Doyle, The Blanched Soldier (“…when you have eliminated all which is impossible, then whatever remains, however improbable, must be the truth.”); Doyle, The Beryl Coronet (“It is an old maxim of mine that when you have excluded the impossible, whatever remains, however improbable, must be the truth.”); Doyle, The Hound of the Baskervilles (1902) (“We balance probabilities and choose the most likely. It is the scientific use of the imagination.”); Doyle, The Sign of the Four, ch 6 (1890)(“‘You will not apply my precept’, he said, shaking his head. ‘How often have I said to you that when you have eliminated the impossible, whatever remains, however improbable, must be the truth? We know that he did not come through the door, the window, or the chimney. We also know that he could not have been concealed in the room, as there is no concealment possible. When, then, did he come?”)

[16] Matrixx Initiatives, Inc. v. Siracusano, 131 S. Ct. 1309, 1319 (2011). 

[17] The citation to Wells was clearly wrong in that the plaintiffs in that case had, in fact, relied upon studies that were nominally statistically significant, and so the Wells court could not have held that statistical significance was unnecessary.

[18] Best v. Lowe’s Home Centers, Inc., 563 F.3d 171, 179, 183-84 (6th Cir. 2009).

[19] See generally Harold C. Sox, Michael C. Higgins, and Douglas K. Owens, Medical Decision Making (2d ed. 2014). 

Posted in Causation, Reference Manual on Scientific Evidence, Rule 702 | 2 Comments »

Reference Manual – Desiderata for 4th Edition – Part I – Signature Diseases

January 30th, 2023

The fourth edition of the Reference Manual on Scientific Evidence is by all accounts under way. Each of the first three editions represented an improvement over previous editions, but the last edition continued to have substantive problems. The bar, the judiciary, and the scientific community hopefully await an improved fourth edition. Although I have posted previously about issues in the third edition, I am updating and adding to what I have written.[1]  There were only a few reviews and acknowledgments of the third edition.[2] The editorial staff provided little to no opportunity for comments in advance of the third edition, and to date, there has been no call for public comment about the pending fourth edition. I hope there will be more opportunity for the legal and scientific community to comment in the production of the fourth edition.

There are several issues raised by the third edition’s treatment of specific causation, which I hope will be improved in the fourth edition. One such issue is the epidemiology chapter’s brief discussion of so-called signature diseases. The chapter takes the curious position that epidemiology has nothing to say about individual or specific causation, a position I will discuss in later posts. The chapter, however, carves out a limited exception to its (questionable) edict that epidemiology does not concern itself with specific causation.  The chapter tells us, uncontroversially, that some diseases do not occur without exposure to a specific chemical or substance. In my view, the authors of this chapter then go astray in telling us that “[a]bestosis is a signature disease for asbestos, and vaginal adenocarcinoma (in young adult women) is a signature disease for in utero DES exposure.”

Now, by definition, only asbestos can cause asbestosis, but asbestosis presents clinically in a way that is indistinguishable in many cases from idiopathic pulmonary fibrosis and other interstitial fibrotic diseases of the lungs. Over the years, the diagnostic criteria for asbestosis have changed, but these criteria have always had a specificity and sensitivity less than 100%. Saying that a case of asbestosis must have been caused by asbestos begs the clinical question whether the case really is asbestosis.

The chapter’s characterization of vaginal adenocarcinoma as a signature disease of in utero DES exposure is also not correct.  Although this cancer in young women is extremely rare, there is a baseline risk that allows the calculation of relative risks for young women exposed in utero. In older women, the relative risks are lower because the baseline risks are higher, and because the effect of DES is diminished for older onset cases.[3] The disease was known before the use of DES in pregnant women began after World War II.[4]

For support of their discussion of “signal diseases,” the authors of the epidemiology chapter chose, remarkably, to cite an article that was over 25 years old (now over 35 years old) at the time the third edition was published.[5] The referenced passage asks us to:

“Consider tort claims for what have come to be called signature disease. These are diseases characteristically caused by only a few substances – such as the vaginal adenocarcinoma usually associated with exposure to DES in utero – and mesothelioma, a cancer of the pleura caused almost exclusively by exposure to asbestos fibers in the air.”[6]

Well, “usually associated” does equal signature disease.[7] The relative risks for smoking and some kinds of lung cancer are higher than for DES in utero and clear cell vaginal adenocarcinoma, but no one calls lung cancer a signature disease of smoking. (Admittedly, smoking is the major cause and perhaps the most preventable cause of lung cancer in Western countries.)

The third edition’s reference to a source that describes mesothelioma as “caused almost exclusively by exposure to asbestos fibers” is also out of date.[8] Recognizing that casual comments and citations can influence credulous judges, the authors of the fourth edition should strive for greater accuracy in their discussions of such scientific issues. It may be time to find new examples of signature disease.

[1] “Reference Manual on Scientific Evidence v4.0” (Feb. 28, 2021); “Reference Manual on Scientific Evidence – 3rd Edition is Past Its Expiry” (Oct. 17, 2021). 

[2] See, e.g., Adam Dutkiewicz, “Book Review: Reference Manual on Scientific Evidence, Third Edition,” 28 Thomas M. Cooley L. Rev. 343 (2011); John A. Budny, “Book Review: Reference Manual on Scientific Evidence, Third Edition,” 31 Internat’l J. Toxicol. 95 (2012); James F. Rogers, Jim Shelson, and Jessalyn H. Zeigler, “Changes in the Reference Manual on Scientific Evidence (Third Edition),” Internat’l Ass’n Def. Csl. Drug, Device & Biotech. Comm. Newsltr. (June 2012). See Schachtman “New Reference Manual’s Uneven Treatment of Conflicts of Interest” (Oct. 12, 2011).

[3] Janneke Verloop, Flora E. van Leeuwen, Theo J. M. Helmerhorst, Hester H. van Boven, and Matti A. Rookus, “Cancer risk in DES daughters,” 21 Cancer Causes & Control 999 (2010).

[4] See “Risk Factors for Vaginal Cancer,” American Cancer Soc’y website (last visited Jan. 29, 2023).

[5] Kenneth S. Abraham & Richard A. Merrill, Scientific Uncertainty in the Courts, 2 Issues Sci. & Tech. 93, 101 (Winter 1986).

[6] Id.

[7] See, e.g., Kadir Güzin, Semra Kayataş Eserm, Ayşe Yiğit, and Ebru Zemheri, “Primary clear cell carcinoma of the vagina that is not related to in utero diethylstilbestrol use,” 3 Gynecol. Surg. 281 (2006).

[8] Michele Carbone, Harvey I. Pass, Guntulu Ak, H. Richard Alexander Jr., Paul Baas, Francine Baumann, Andrew M. Blakely, Raphael Bueno, Aleksandra Bzura, Giuseppe Cardillo, Jane E. Churpek, Irma Dianzani, Assunta De Rienzo, Mitsuru Emi, Salih Emri, Emanuela Felley-Bosco, Dean A. Fennell, Raja M. Flores, Federica Grosso, Nicholas K. Hayward, Mary Hesdorffer, Chuong D. Hoang, Peter A. Johansson, Hedy L. Kindler, Muaiad Kittaneh, Thomas Krausz, Aaron Mansfield, Muzaffer Metintas, Michael Minaai, Luciano Mutti, Maartje Nielsen, Kenneth O’Byrne, Isabelle Opitz, Sandra Pastorino, Francesca Pentimalli, Marc de Perrot, Antonia Pritchard, Robert Taylor Ripley, Bruce Robinson, and Valerie Rusch, “Medical and Surgical Care of Patients With Mesothelioma and Their Relatives Carrying Germline BAP1 Mutations,” 17 J. Thoracic Oncol. 873 (2022). See also Mitchell Cheung, Yuwaraj Kadariya, Eleonora Sementino, Michael J. Hall, Ilaria Cozzi, Valeria Ascoli, Jill A. Ohar, and Joseph R. Testa, “Novel LRRK2 mutations and other rare, non-BAP1-related candidate tumor predisposition gene variants in high-risk cancer families with mesothelioma and other tumors,” 30 Human Molecular Genetics 1750 (2021); Thomas Wiesner, Isabella Fried, Peter Ulz, Elvira Stacher, Helmut Popper, Rajmohan Murali, Heinz Kutzner, Sigurd Lax, Freya Smolle-Jüttner, Jochen B. Geigl, and Michael R. Speicher, “Toward an Improved Definition of the Tumor Spectrum Associated With BAP1 Germline Mutations,” 30 J. Clin. Oncol. e337 (2012); Alexandra M. Haugh, BA1; Ching-Ni Njauw, MS2,3; Jeffrey A. Bubley, et al., “Genotypic and Phenotypic Features of BAP1 Cancer Syndrome: A Report of 8 New Families and Review of Cases in the Literature,” 153 J.Am. Med. Ass’n Dermatol. 999 (2017).

Posted in Asbestos, Causation, Reference Manual on Scientific Evidence | 6 Comments »

An Opinion to SAVOR

November 11th, 2022

The saxagliptin medications are valuable treatments for type 2 diabetes mellitus (T2DM). The SAVOR (Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus) study was a randomized controlled trial, undertaken by manufacturers at the request of the FDA.[1] As a large (over sixteen thousand patients randomized) double-blinded cardiovascular outcomes trial, SAVOR collected data on many different end points in patients with T2DM, at high risk of cardiovascular disease, over a median of 2.1 years. The primary end point was a composite end point of cardiac death, non-fatal myocardial infarction, and non-fatal stroke. Secondary end points included each constituent of the composite, as well as hospitalizations for heart failure, coronary revascularization, or unstable angina, as well as other safety outcomes.

The SAVOR trial found no association between saxagliptin use and the primary end point, or any of the constituents of the primary end point.  The trial did, however, find a modest association between saxagliptin and one of the several secondary end points, hospitalization for heart failure (hazard ratio, 1.27; 95% C.I., 1.07 to 1.51; p = 0.007). The SAVOR authors urged caution in interpreting their unexpected finding for heart failure hospitalizations, given the multiple end points considered.[2] Notwithstanding the multiplicity, in 2016, the FDA, which does not require a showing of causation for adding warnings to a drug’s labeling, added warnings about the “risk” of hospitalization for heart failure from the use of saxagliptin medications.

And the litigation came.

The litigation evidentiary display grew to include, in addition to SAVOR, observational studies, meta-analyses, and randomized controlled trials of other DPP-4 inhibitor medications that are in the same class as saxagliptin. The SAVOR finding for heart failure was not supported by any of the other relevant human study evidence. The lawsuit industry, however, armed with an FDA warning, pressed its cases. A multi-district litigation (MDL 2809) was established. Rule 702 motions were filed by both plaintiffs’ and defendants’ counsel.

When the dust settled in this saxagliptin litigation, the court found that the defendants’ expert witnesses satisfied the relevance and reliability requirements of Rule 702, whereas the proferred opinions of plaintiff’s expert witness, Dr. Parag Goyal, a cardiologist at Cornell-Weill Hospital in New York, did not satisfy Rule 702.[3] The court’s task was certainly made easier by the lack of any other expert witness or published opinion that saxagliptin actually causes heart failure serious enough to result in hospitalization. 

The saxagliptin litigation presented an interesting array of facts for a Rule 702 show down. First, there was an RCT that reported a nominally statistically significant association between medication use and a harm, hospitalization for heart failure. The SAVOR finding, however, was in a secondary end point, and its statistical significance was unimpressive when considered in the light of the multiple testing that took place in the context of a cardiovascular outcomes trial.

Second, the heart failure increase was not seen in the original registration trials. Third, there was an effort to find corroboration in observational studies and meta-analyses, without success. Fourth, there was no apparent mechanism for the putative effect. Fifth, there was no support from trials or observational studies of other medications in the class of DPP-4 inhibitors.

Dr. Goyal testified that the heart failure finding in SAVOR “should be interpreted as cause and effect unless there is compelling evidence to prove otherwise.” On this record, the MDL court excluded Dr. Goyal’s causation opinions. Dr. Goyal purported to conduct a Bradford Hill analysis, but the MDL court appeared troubled by his glib dismissal of the threat to validity in SAVOR from multiple testing, and his ignoring the consistency prong of the Hill factors. SAVOR was the only heart failure finding in humans, with the remaining observational studies, meta-analyses, and other trials of DPP-4 inhibitors failing to provide supporting evidence.

The challenged defense expert witnesses defended the validity of their opinions, and ultimately the MDL court had little concern in permitting them through the judicial gate. The plaintiffs’ challenges to Suneil Koliwad, a physician with a doctorate in molecular physiology, Eric Adler, a cardiologist, and Todd Lee, a pharmaco-epidemiologist, were all denied. The plaintiffs challenged, among other things, whether Dr. Adler was qualified to apply a Bonferroni correction to the SAVOR results, and whether Dr. Lee was obligated to obtain and statistically analyze the data from the trials and studies ab initio. The MDL court quickly dispatched these frivolous challenges.

The saxagliptin MDL decision is an important reminder that litigants should remain vigilant about inaccurate assertions of “statistical significance,” even in premier, peer-reviewed journals. Not all journals are as careful as the New England Journal of Medicine in requiring qualification of claims of statistical significance in the face of multiple testing.

One legal hiccup in the court’s decision was its improvident citation to Daubert, for the proposition that the gatekeeping inquiry must focus “solely on principles and methodology, not on the conclusions they generate.”[4] That piece of obiter dictum did not survive past the Supreme Court’s 1997 decision in Joiner,[5] and it was clearly superseded by statute in 2000. Surely it is time to stop citing Daubert for this dictum.

[1] Benjamin M. Scirica, Deepak L. Bhatt, Eugene Braunwald, Gabriel Steg, Jaime Davidson, et al., for the SAVOR-TIMI 53 Steering Committee and Investigators, “Saxagliptin and Cardiovascular Outcomes in Patients with Type 2 Diabetes Mellitus,” 369 New Engl. J. Med. 1317 (2013).

[2] Id. at 1324.

[3] In re Onglyza & Kombiglyze XR Prods. Liab. Litig., MDL 2809, 2022 WL 43244 (E.D. Ken. Jan. 5, 2022).

[4] Daubert v. Merrell Dow Pharms., Inc., 509 U.S. 579, 595 (1993).

[5] General Electric Co. v. Joiner, 522 U.S. 136 (1997).

Posted in Causation, Meta-analysis, Rule 702, Scientific Evidence, statistical evidence | No Comments »

Cheng’s Proposed Consensus Rule for Expert Witnesses

September 15th, 2022

Edward K. Cheng is the Hess Professor of Law in absentia from Vanderbilt Law School, while serving this fall as a visiting professor at Harvard. Professor Cheng is one of the authors of the multi-volume treatise, Modern Scientific Evidence, and the author of many articles on scientific and statistical evidence. Cheng’s most recent article, “The Consensus Rule: A New Approach to Scientific Evidence,”[1] while thought provoking, follows in the long-standing tradition of law school professors to advocate evidence law reforms, based upon theoretical considerations devoid of practical or real-world support.

Cheng’s argument for a radical restructuring of Rule 702 is based upon his judgment that jurors and judges are epistemically incompetent to evaluate expert witness opinion testimony. The current legal approach has trial judges acting as gatekeepers of expert witness testimony, and jurors acting as judges of factual scientific claims. Cheng would abolish these roles as beyond their ken.[2] Lay persons can, however, determine which party’s position is supported by the relevant expert community, which he presumes (without evidence) possesses the needed epistemic competence. Accordingly, Cheng would rewrite the legal system’s approach to important legal disputes, such as disputes over causal claims, from:

Whether a given substance causes a given disease

to

Whether the expert community believes that a given substance causes a given disease.

Cheng channels the philosophical understanding of the ancients who realized that one must have expertise to judge whether someone else has used that expertise correctly. And he channels the contemporary understanding that knowledge is a social endeavor, not the unique perspective of an individual in isolation. From these twin premisses, Cheng derives a radical and cynical proposal to reform the law of expert witness testimony. In his vision, experts would come to court not to give their own opinions, and certainly not to try to explain how they arrive at their opinions from the available evidence. For him, the current procedure is too much like playing chess with a monkey. The expert function would consist of telling the jury what the expert witness’s community believes.[3] Jurors would not decide the “actual substantive questions,” but simply decide what they believe the relevant expert witness community accepts as a consensus. This radical restructuring is what Cheng calls the “consensus rule.”

In this proposed “consensus rule,” there is no room for gatekeeping. Parties continue to call expert witnesses, but only as conduits for the “consensus” opinions of their fields. Indeed, Cheng’s proposal would radically limit expert witness to service as pollsters; their testimony would present only their views of what the consensus is in their fields. This polling information is the only evidence that the jury hear from expert witnesses, because this is the only evidence that Cheng believes the jury is epistemically competent to assess.[4]

Under Cheng’s Consensus Rule, when there is no consensus in the realm, the expert witness regime defaults to “anything goes,” without gatekeeping.[5] Judges would continue to exercise some control over who is qualified to testify, but only as far as the proposed experts must be in a position to know what the consensus is in their fields.

Cheng does not explain why, under his proposed “consensus rule,” subject matter experts are needed at all.  The parties might call librarians, or sociologists of science, to talk about the relevant evidence of consensus. If a party cannot afford a librarian expert witness, then perhaps lawyers could present directly the results of their PubMed, and other internet searches.

Cheng may be right that his “deferential approach” would eliminate having the inexpert passing judgment on the expert. The “consensus rule” would reduce science to polling, conducted informally, often without documentation or recording, by partisan expert witnesses. This proposal hardly better reflects, as he argues, the “true” nature of science. In Cheng’s vision, science in the courtroom is just a communal opinion, without evidence and without inference. To be sure, this alternative universe is tidier and less disputatious, but it is hardly science or knowledge. We are left with opinions about opinions, without data, without internal or external validity, and without good and sufficient facts and data.

Cheng claims that his proposed Consensus Rule is epistemically superior to Rule 702 gatekeeping. For the intellectual curious and able, his proposal is a counsel of despair. Deference to the herd, he tells us “is not merely optimal—it is the only practical strategy.”[6] In perhaps the most extreme overstatement of his thesis, Cheng tells us that

“deference is arguably not due to any individual at all! Individual experts can be incompetent, biased, error prone, or fickle—their personal judgments are not and have never been the source of reliability. Rather, proper deference is to the community of experts, all of the people who have spent their careers and considerable talents accumulating knowledge in their field.”[7]

Cheng’s hypothesized community of experts, however is worthy of deference only by virtue of the soundness of its judgments. If a community has not severely tested its opinions, then its existence as a community is irrelevant. Cheng’s deference is the sort of phenomenon that helped create Lysenkoism and other intellectual fads that were beyond challenge with actual data.

There is, I fear, some partial truth to Cheng’s judgment of juries and judges as epistemically incompetent, or challenged, to judge science, but his judgment seems greatly overstated. Finding aberrant jury verdicts would be easy, but Cheng provides no meaningful examples of gatekeeping gone wrong. Professor Cheng may have over-generalized in stating that judges are epistemically incompetent to make substantive expert determinations. He surely cannot be suggesting that judges never have sufficient scientific acumen to determine the relevance and reliability of expert witness opinion. If judges can, in some cases, make a reasonable go at gatekeeping, why then is Cheng advocating a general rule that strips all judges of all gatekeeping responsibility with respect to expert witnesses?

Clearly judges lack the technical resources, time, and background training to delve deeply into the methodological issues with which they may be confronted. This situation could be ameliorated by budgeting science advisors and independent expert witnesses, and by creating specialty courts staffed with judges that have scientific training. Cheng acknowledges this response, but he suggests that conflicts with “norms about generalist judges.”[8] This retreat to norms is curious in the face of Cheng’s radical proposals, and the prevalence of using specialist judges for adjudicating commercial and patent disputes.

Although Cheng is correct that assessing validity and reliability of scientific inferences and conclusions often cannot be reduced to a cookbook or checklist approach, not all expertise is as opaque as Cheng suggests. In his view, lawyers are deluded into thinking that they can understand the relevant science, with law professors being even worse offenders.[9] Cross-examining a technical expert witness can be difficult and challenging, but lawyers on both sides of the aisle occasionally demolish the most skilled and knowledgeable expert witnesses, on substantive grounds. And these demolitions happen to expert witnesses who typically, self-servingly claim that they have robust consensuses agreeing with their opinions.

While scolding us that we must get “comfortable with relying on the expertise and authority of others,” Cheng reassures us that deferring to authority is “not laziness or an abdication of our intellectual responsibility.”[10] According to Cheng, the only reason to defer to the opinion of expert is that they are telling us what their community would say.[11] Good reasons, sound evidence, and valid inference need not worry us in Cheng’s world.

Finding Consensus

Cheng tells us that his Consensus Rule would look something like:

“Rule 702A. If the relevant scientific community believes a fact involving specialized knowledge, then that fact is established accordingly.”

Imagine the endless litigation over what the “relevant” community is. For a health effect claim about a drug and heart attacks, is it the community of cardiologists or epidemiologists? Do we accept the pronouncements of the American Heart Association or those of the American College of Cardiology. If there is a clear consensus based upon a clinical trial, which appears to be based upon suspect data, is discovery of underlying data beyond the reach of litigants because the correctness of the allegedly dispositive study is simply not in issue? Would courts have to take judicial notice of the clear consensus and shut down any attempt to get to the truth of the matter?

Cheng acknowledges that cases will involve issues that are controversial or undeveloped, without expert community consensus. Many litigations start after publication of a single study or meta-analysis, which is hardly the basis for any consensus. Cheng appears content, in this expansive area, to revert to anything goes because if the expert community has not coalesced around a unified view, or if the community is divided, then the courts cannot do better than flipping a coin! Cheng’s proposal thus has a loophole the size of the Sun.

Cheng tells us, unhelpfully, that “[d]etermining consensus is difficult in some cases, and less so in others.”[12] Determining consensus may not be straightforward, but no matter. Consensus Rule questions are not epistemically challenging and thus “far more manageable,” because they requires no special expertise. (Again, why even call a subject matter expert witness, as opposed to a science journalist or librarian?) Cheng further advises that consensus is “a bit like the reasonable person standard in negligence,” but this simply conflates normative judgments with the scientific judgments.[13]

Cheng’s Consensus Rule would allow the use of a systematic review or a meta-analysis, not for evidence of the correctness of its conclusions, but only as evidence of a consensus.[14] The thought experiment of how this suggestion plays out in the real world may cause some agita. The litigation over Avandia began within days of the publication of a meta-analysis in the New England Journal of Medicine.[15] So some evidence of consensus; right? But then the letters to the editor within a few weeks of publication showed that the meta-analysis was fatally flawed. Inadmissible! Under the Consensus Rule the correctness or the methodological appropriateness of the meta-analysis is irrelevant. A few months later, another meta-analysis is published, which fails to find the risk that the original meta-analysis claimed. Is the trial now about which meta-analysis represents the community’s consensus, or are we thrown into the game of anything goes, where expert witnesses just say things, without judicial supervision?  A few years go by, and now there is a large clinical trial that supersedes all the meta-analyses of small trials.[16] Is a single large clinical trial now admissible as evidence of a new consensus, or are only systematic reviews and meta-analyses relevant evidence?

Cheng’s Consensus Rule will be useless in most determinations of specific causation.  It will be a very rare case indeed when a scientific organization issues a consensus statement about plaintiff John Doe. Very few tort cases involve putative causal agents that are thought to cause every instance of some disease in every person exposed to the agent. Even when a scientific community has addressed general causation, it will have rarely resolved all the uncertainty about the causal efficacy of all levels of exposure or the appropriate window of latency. So Cheng’s proposal guarantees to remove specific causation from the control of Rule 702 gatekeeping.

The potential for misrepresenting consensus is even greater than the misrepresentations of actual study results. At least the data are the data, but what will jurors do when they are regaled by testimony about the informal consensus reached in the hotel lobby of the latest scientific conference. Regulatory pronouncements that are based upon precautionary principles will be misrepresented as scientific consensus.  Findings by the International Agency for Research on Cancer that a substance is a IIA “probable human carcinogen” will be hawked as a consensus, even though the classification specifically disclaims any quantitative meaning for “probable,” and it directly equates to “insufficient” evidence of carcinogencity in humans.

In some cases, as Cheng notes, organizations such as the National Research Council, or the National Academy of Science, Engineering and Medicine (NASEM), will have weighed in on a controversy that has found its way into court.[17] Any help from such organizations will likely be illusory. Consider the 2006 publication of a comprehensive review of the available studies on non-pulmonary cancers and asbestos exposure by NASEM. The writing group presented its assessment of colorectal cancer as not causally associated with occupational asbestos exposure.[18] By 2007, the following year, expert witnesses for plaintiffs argued that the NASEM publication was no longer a consensus because one or two (truly inconsequential studies) had been published after the report and thus not considered. Under Cheng’s proposal, this dodge would appear to be enough to oust the consensus rule, and default to the “anything goes” rule. The scientific record can change rapidly, and many true consensus statements quickly find their way into the dustbin of scientific history.

Cheng greatly underestimates the difficulty in ascertaining “consensus.” Sometimes, to be sure, professional societies issue consensus statements, but they are often tentative and inconclusive. In many areas of science, there will be overlapping realms of expertise, with different disciplines issuing inconsistent “consensus” statements. Even within a single expert community, there may be two schools of thoughts about a particular issue.

There are instances, perhaps more than a few, when a consensus is epistemically flawed. If, as is the case in many health effect claims, plaintiffs rely upon the so-called linear no-threshold dose-response (LNT) theory of carcinogenesis, plaintiffs will point to regulatory pronouncements that embrace LNT as “the consensus.” When scientists are being honest, they generally recognize LNT as part of a precautionary principle approach, which may make sense as the foundation of “risk assessment.” The widespread assumption of LNT in regulatory agencies, and among scientists who work in such agencies, is understandable, but LNT remains an assumption. Nonetheless, we already see LNT hawked as a consensus, which under Cheng’s Consenus Rule would become the key dispositive issue, while quashing the mountain of evidence that there are, in fact, defense mechanisms to carcinogenesis that result in practical thresholds.

Beyond, regulatory pronouncements, some areas of scientific endeavor have themselves become politicized and extremist. Tobacco smoking surely causes lung cancer, but the studies of environmental tobacco smoking and lung cancer have been oversold. In areas of non-scientific disputes, such as history of alleged corporate malfeasance, juries will be treated to “the consensus” of Marxist labor historians, without having to consider the actual underlying historical documents. Cheng tells us that his Consensus Rule is a “realistic way of treating nonscientific expertise,”[19] which would seem to cover historian expert witness. Yet here, lawyers and lay fact finders are fully capable of exploring the glib historical conclusions of historian witnesses with cross-examination on the underlying documentary facts of the proffered opinions.

The Alleged Warrant for the Consensus Rule

If Professor Cheng is correct that the current judicial system, with decisions by juries and judges, is epistemically incompetent, does his Consensus Rule necessarily follow?  Not really. If we are going to engage in radical reforms, then the institutionalization of blue-ribbon juries would make much greater sense. As for Cheng’s claim that knowledge is “social,” the law of evidence already permits the use of true consensus statements as learned treatises, both to impeach expert witnesses who disagree, and (in federal court) to urge the truth of the learned treatise.

The gatekeeping process of Rule 702, which Professor Cheng would throw overboard, has important advantages in that judges ideally will articulate reasons for finding expert witness opinion testimony admissible or not. These reasons can be evaluated, discussed, and debated, with judges, lawyers, and the public involved. This gatekeeping process is rational and socially open.

Some Other Missteps in Cheng’s Argument

Experts on Both Sides are Too Extreme

Cheng’s proposal is based, in part, upon his assessment that the adversarial system causes the parties to choose expert witnesses “at the extremes.” Here again, Cheng provides no empirical evidence for his assessment. There is a mechanical assumption often made by people who do not bother to learn the details of a scientific dispute that the truth must somehow lie in the “middle.” For instance, in MDL 926, the silicone gel breast implant litigation, presiding Judge Sam Pointer complained about the parties’ expert witnesses being too extreme. Judge Pointer  believed that MDL judges should not entertain Rule 702 challenges, which were in his view properly heard by the transferor courts. As a result, Judge Robert Jones, and then Judge Jack Weinstein, conducted thorough Rule 702 hearings and found that the plaintiffs’ expert witnesses’ opinions were unreliable and insufficiently supported by the available evidence.[20] Judge Weinstein started the process of selecting court-appointed expert witnesses for the remaining New York cases, which goaded Judge Pointer into taking the process back to the MDL court level. After appointing four, highly qualified expert witnesses, Judge Pointer continued to believe that the parties’ expert witnesses were “extremists,” and that the courts’ own experts would come down somewhere between them.  When the court-appointed experts filed their reports, Judge Pointer was shocked that all four of his experts sided with the defense in rejecting the tendentious claims of plaintiffs’ expert witnesses.

Statistical Significance

Along the way, in advocating his radical proposal, Professor Cheng made some other curious announcements. For instance, he tells us that “[w]hile historically used as a rule of thumb, statisticians have now concluded that using the 0.05 [p-value] threshold is more distortive than helpful.”[21] Cheng’s purpose here is unclear, but the source he cited does not remotely support his statement, and certainly not his gross overgeneralization about “statisticians.” If this is the way he envisions experts will report “consensus,” then his program seems broken at its inception. The American Statistical Association’s (ASA) p-value “consensus” statement articulated six principles, the third of which noted 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 a few light years away from statisticians’ concluding that statistical significance thresholds are more distortive than helpful. The ASA p-value statement further explains that

“[t]he widespread use of ‘statistical significance’ (generally interpreted as ‘p < 0.05’) as a license for making a claim of a scientific finding (or implied truth) leads to considerable distortion of the scientific process.”[22]

In the science of health effects, statistical significance remains extremely important, but it has never been a license for making causal claims. As Sir Austin Bradford Hill noted in his famous after-dinner speech, ruling out chance (and bias) as an explanation for an association was merely a predicate for evaluating the association for causality.[23]

Over-endorsing Animal Studies

Under Professor Cheng’s Consensus Rule, the appropriate consensus might well be one generated solely by animal studies. Cheng tells that “perhaps” scientists do not consider toxicology when the pertinent epidemiology is “clear.” When the epidemiology, however, is unclear, scientists consider toxicology.[24] Well, of course, but the key question is whether a consensus about causation in humans will be based upon non-human animal studies. Cheng seems to answer this question in the affirmative by criticizing courts that have required epidemiologic studies “even though the entire field of toxicology uses tissue and animal studies to make inferences, often in combination with and especially in the absence of epidemiology.”[25] The vitality of the field of toxicology is hardly undermined by its not generally providing sufficient grounds for judgments of human causation.

Relative Risk Greater Than Two

In the midst of his argument for the Consensus Rule, Cheng points critically to what he calls “questionable proxies” for scientific certainty. One such proxy is the judicial requirement of risk ratios in excess of two. His short discussion appears to be focused upon the inference of specific causation in a given case, but it leads to a non-sequitur:

“Some courts have required a relative risk of 2.0 in toxic tort cases, requiring a doubling of the population risk before considering causation.73 But the preponderance standard does not require that the substance more likely than not caused any case of the disease in the population, it requires that the substance more likely than not caused the plaintiff’s case.”[26]

Of course, it is exactly because we are interested in the probability of causation of the plaintiff’s case, that we advert to the risk ratio to give us some sense whether “more likely than not” the exposure caused plaintiff’s case. Unless plaintiff can show he is somehow unique, he is “any case.” In many instances, plaintiff cannot show how he is different from the participants of the study that gave rise to the risk ratio less than two.

[1] Edward K. Cheng, “The Consensus Rule: A New Approach to Scientific Evidence,” 75 Vanderbilt L. Rev. 407 (2022) [Consensus Rule].

[2] Consensus Rule at 410 (“The judge and the jury, lacking in expertise, are not competent to handle the questions that the Daubert framework assigns to them.”)

[3] Consensus Rule at 467 (“Under the Consensus Rule, experts no longer offer their personal opinions on causation or teach the jury how to assess the underlying studies. Instead, their testimony focuses on what the expert community as a whole believes about causation.”)

[4] Consensus Rule at 467.

[5] Consensus Rule at 437.

[6] Consensus Rule at 434.

[7] Consensus Rule at 434.

[8] Consensus Rule at 422.

[9] Consensus Rule at 429.

[10] Consensus Rule at 432-33.

[11] Consensus Rule at 434.

[12] Consensus Rule at 456.

[13] Consensus Rule at 457.

[14] Consensus Rule at 459.

[15] Steven E. Nissen, M.D., and Kathy Wolski, M.P.H., “Effect of Rosiglitazone on the Risk of Myocardial Infarction and Death from Cardiovascular Causes,” 356 New Engl. J. Med. 2457 (2007).

[16] P.D. Home, et al., “Rosiglitazone Evaluated for Cardiovascular Outcomes in Oral Agent Combination Therapy for Type 2 Diabetes (RECORD), 373 Lancet 2125 (2009).

[17] Consensus Rule at 458.

[18] Jonathan M. Samet, et al., Asbestos: Selected Health Effects (2006).

[19] Consensus Rule at 445.

[20] Hall v. Baxter Healthcare Corp., 947 F. Supp.1387 (D. Or. 1996) (excluding plaintiffs’ expert witnesses’ causation opinions); In re Breast Implant Cases, 942 F. Supp. 958 (E. & S.D.N.Y. 1996) (granting partial summary judgment on claims of systemic disease causation).

[21] Consenus Rule at 424 (citing Ronald L. Wasserstein & Nicole A. Lazar, “The ASA Statement on p-Values: Context, Process, and Purpose,” 70 Am. Statistician 129, 131 (2016)).

[22] Id.

[23] Austin Bradford Hill, “The Environment and Disease: Association or Causation?” 58 Proc. Royal Soc’y Med. 295, 295 (1965). See Schachtman, “Ruling Out Bias & Confounding is Necessary to Evaluate Expert Witness Causation Opinions” (Oct. 29, 2018); “Woodside & Davis on the Bradford Hill Considerations” (Aug. 23, 2013); Frank C. Woodside, III & Allison G. Davis, “The Bradford Hill Criteria: The Forgotten Predicate,” 35 Thomas Jefferson L. Rev. 103 (2013).

[24] Consensus Rule at 444.

[25] Consensus Rule at 424 & n. 74 (citing to one of multiple court advisory expert witnesses in Hall v. Baxter Healthcare Corp., 947 F. Supp.1387, 1449 (D. Or. 1996), who suggested that toxicology would be appropriate to consider when the epidemiology was not clear). Citing to one outlier advisor is a rather strange move for Cheng considering that the “consensus” was readily discernible to the trial judge in Hall, and to Judge Jack Weinstein, a few months later, in In re Breast Implant Cases, 942 F. Supp. 958 (E. & S.D.N.Y. 1996).

[26] Consensus Rule at 424 & n. 73 (citing Lucinda M. Finley, “Guarding the Gate to the Courthouse: How Trial Judges Are Using Their Evidentiary Screening Role to Remake Tort Causation Rules,” 49 Depaul L. Rev. 335, 348–49 (2000). See Schachtman, “Rhetorical Strategy in Characterizing Scientific Burdens of Proof” (Nov. 15, 2014).

Posted in Causation, Expert Witnesses, Rule 702, Scientific Evidence | 3 Comments »

Amicus Curious – Gelbach’s Foray into Lipitor Litigation

August 25th, 2022

Professor Schauer’s discussion of statistical significance, covered in my last post,[1] is curious for its disclaimer that “there is no claim here that measures of statistical significance map easily onto measures of the burden of proof.” Having made the disclaimer, Schauer proceeds to falls into the transposition fallacy, which contradicts his disclaimer, and, generally speaking, is not a good thing for a law professor eager to advance the understanding of “The Proof,” to do.

Perhaps more curious than Schauer’s error is his citation support for his disclaimer.[2] The cited paper by Jonah B. Gelbach is one of several of Gelbach’s papers that advances the claim that the p-value does indeed map onto posterior probability and the burden of proof. Gelbach’s claim has also been the center piece in his role as an advocate in support of plaintiffs in the Lipitor (atorvastatin) multi-district litigation (MDL) over claims that ingestion of atorvastatin causes diabetes mellitus.

Gelbach’s intervention as plaintiffs’ amicus is peculiar on many fronts. At the time of the Lipitor litigation, Sonal Singh was an epidemiologist and Assistant Professor of Medicine, at the Johns Hopkins University. The MDL trial court initially held that Singh’s proffered testimony was inadmissible because of his failure to consider daily dose.[3] In a second attempt, Singh offered an opinion for 10 mg daily dose of atorvastatin, based largely upon the results of a clinical trial known as ASCOT-LLA.[4]

The ASCOT-LLA trial randomized 19,342 participants with hypertension and at least three other cardiovascular risk factors to two different anti-hypertensive medications. A subgroup with total cholesterol levels less than or equal to 6.5 mmol./l. were randomized to either daily 10 mg. atorvastatin or placebo.  The investigators planned to follow up for five years, but they stopped after 3.3 years because of clear benefit on the primary composite end point of non-fatal myocardial infarction and fatal coronary heart disease. At the time of stopping, there were 100 events of the primary pre-specified outcome in the atorvastatin group, compared with 154 events in the placebo group (hazard ratio 0.64 [95% CI 0.50 – 0.83], p = 0.0005).

The atorvastatin component of ASCOT-LLA had, in addition to its primary pre-specified outcome, seven secondary end points, and seven tertiary end points.  The emergence of diabetes mellitus in this trial population, which clearly was at high risk of developing diabetes, was one of the tertiary end points. Primary, secondary, and tertiary end points were reported in ASCOT-LLA without adjustment for the obvious multiple comparisons. In the treatment group, 3.0% developed diabetes over the course of the trial, whereas 2.6% developed diabetes in the placebo group. The unadjusted hazard ratio was 1.15 (0.91 – 1.44), p = 0.2493.[5] Given the 15 trial end points, an adjusted p-value for this particular hazard ratio, for diabetes, might well exceed 0.5, and even approach 1.0.

On this record, Dr. Singh honestly acknowledged that statistical significance was important, and that the diabetes finding in ASCOT-LLA might have been the result of low statistical power or of no association at all. Based upon the trial data alone, he testified that “one can neither confirm nor deny that atorvastatin 10 mg is associated with significantly increased risk of type 2 diabetes.”[6] The trial court excluded Dr. Singh’s 10mg/day causal opinion, but admitted his 80mg/day opinion. On appeal, the Fourth Circuit affirmed the MDL district court’s rulings.[7]

Jonah Gelbach is a professor of law at the University of California at Berkeley. He attended Yale Law School, and received his doctorate in economics from MIT.

Professor Gelbach entered the Lipitor fray to present a single issue: whether statistical significance at conventionally demanding levels such as 5 percent is an appropriate basis for excluding expert testimony based on statistical evidence from a single study that did not achieve statistical significance.

Professor Gelbach is no stranger to antic proposals.[8] As amicus curious in the Lipitor litigation, Gelbach asserts that plaintiffs’ expert witness, Dr. Singh, was wrong in his testimony about not being able to confirm the ASCOT-LLA association because he, Gelbach, could confirm the association.[9] Ultimately, the Fourth Circuit did not discuss Gelbach’s contentions, which is not surprising considering that the asserted arguments and alleged factual considerations were not only dehors the record, but in contradiction of the record.

Gelbach’s curious claim is that any time a risk ratio, for an exposure and an outcome of interest, is greater than 1.0, with a p-value < 0.5,[10] the evidence should be not only admissible, but sufficient to support a conclusion of causation. Gelbach states his claim in the context of discussing a single randomized controlled trial (ASCOT-LLA), but his broad pronouncements are carelessly framed such that others may take them to apply to a single observational study, with its greater threats to internal validity.

Contra Kumho Tire

To get to his conclusion, Gelbach attempts to remove the constraints of traditional standards of significance probability. Kumho Tire teaches that expert witnesses must “employ[] in the courtroom the same level of intellectual rigor that characterizes the practice of an expert in the relevant field.”[11] For Gelbach, this “eminently reasonable admonition” does not impose any constraints on statistical inference in the courtroom. Statistical significance at traditional levels (p < 0.05) is for elitist scholarly work, not for the “practical” rent-seeking work of the tort bar. According to Gelbach, the inflation of the significance level ten-fold to p < 0.5 is merely a matter of “weight” and not admissibility of any challenged opinion testimony.

Likelihood Ratios and Posterior Probabilities

Gelbach maintains that any evidence that has a likelihood ratio (LR > 1) greater than one is relevant, and should be admissible under Federal Rule of Evidence 401.[12] This argument ignores the other operative Federal Rules of Evidence, namely 702 and 703, which impose additional criteria of admissibility for expert witness opinion testimony.

With respect to variance and random error, Gelbach tells us that any evidence that generates a LR > 1, should be admitted when “the statistical evidence is statistically significant below the 50 percent level, which will be true when the p-value is less than 0.5.”[13]

At times, Gelbach seems to be discussing the admissibility of the ASCOT-LLA study itself, and not the proffered opinion testimony of Dr. Singh. The study itself would not be admissible, although it is clearly the sort of hearsay an expert witness in the field may consider. If Dr. Singh were to have reframed and recalculated the statistical comparisons, then the Rule 703 requirement of “reasonable reliance” by scientists in the field of interest may not have been satisfied.

Gelbach also generates a posterior probability (0.77), which is based upon his calculations from data in the ASCOT-LLA trial, and not the posterior probability of Dr. Singh’s opinion. The posterior probability, as calculated, is problematic on many fronts.

Gelbach does not present his calculations – for the sake of brevity he says – but he tells us that the ASCOT-LLA data yield a likelihood ratio of roughly 1.9, and a p-value of 0.126.[14] What the clinical trialists reported was a hazard ratio of 1.15, which is a weak association on most researchers’ scales, with a two-sided p-value of 0.25, which is five times higher than the usual 5 percent. Gelbach does not explain how or why his calculated p-value for the likelihood ratio is roughly half the unadjusted, two-sided p-value for the tertiary outcome from ASCOT-LLA.

As noted, the reported diabetes hazard ratio of 1.15 was a tertiary outcome for the ASCOT trial, one of 15 calculated by the trialists, with p-values unadjusted for multiple comparisons.  The failure to adjust is perhaps excusable in that some (but certainly not all) of the outcome variables are overlapping or correlated. A sophisticated reader would not be misled; only when someone like Gelbach attempts to manufacture an inflated posterior probability without accounting for the gross underestimate in variance is there an insult to statistical science. Gelbach’s recalculated p-value for his LR, if adjusted for the multiplicity of comparisons in this trial, would likely exceed 0.5, rendering all his arguments nugatory.

Using the statistics as presented by the published ASCOT-LLA trial to generate a posterior probability also ignores the potential biases (systematic errors) in data collection, the unadjusted hazard ratios, the potential for departures from random sampling, errors in administering the participant recruiting and inclusion process, and other errors in measurements, data collection, data cleaning, and reporting.

Gelbach correctly notes that there is nothing methodologically inappropriate in advocating likelihood ratios, but he is less than forthcoming in explaining that such ratios translate into a posterior probability only if he posits a prior probability of 0.5.[15] His pretense to having simply stated “mathematical facts” unravels when we consider his extreme, unrealistic, and unscientific assumptions.

The Problematic Prior

Gelbach’s glibly assumes that the starting point, the prior probability, for his analysis of Dr. Singh’s opinion is 50%. This is an old and common mistake,[16] long since debunked.[17] Gelbach’s assumption is part of an old controversy, which surfaced in early cases concerning disputed paternity. The assumption, however, is wrong legally and philosophically.

The law simply does not hand out 0.5 prior probability to both parties at the beginning of a trial. As Professor Jaffee noted almost 35 years ago:

“In the world of Anglo-American jurisprudence, every defendant, civil and criminal, is presumed not liable. So, every claim (civil or criminal) starts at ground zero (with no legal probability) and depends entirely upon proofs actually adduced.”[18]

Gelbach assumes that assigning “equal prior probability” to two adverse parties is fair, because the fact-finder would not start hearing evidence with any notion of which party’s contentions are correct. The 0.5/0.5 starting point, however, is neither fair nor is it the law.[19] The even odds prior is also not good science.

The defense is entitled to a presumption that it is not liable, and the plaintiff must start at zero.  Bayesians understand that this is the death knell of their beautiful model.  If the prior probability is zero, then Bayes’ Theorem tells us mathematically that no evidence, no matter how large a likelihood ratio, can move the prior probability of zero towards one. Bayes’ theorem may be a correct statement about inverse probabilities, but still be an inadequate or inaccurate model for how factfinders do, or should, reason in determining the ultimate facts of a case.

We can see how unrealistic and unfair Gelbach’s implied prior probability is if we visualize the proof process as a football field.  To win, plaintiffs do not need to score a touchdown; they need only cross the mid-field 50-yard line. Rather than making plaintiffs start at the zero-yard line, however, Gelbach would put them right on the 50-yard line. Since one toe over the mid-field line is victory, the plaintiff is spotted 99.99+% of its burden of having to present evidence to build up 50% probability. Instead, plaintiffs are allowed to scoot from the zero yard line right up claiming success, where even the slightest breeze might give them winning cases. Somehow, in the model, plaintiffs no longer have to present evidence to traverse the first half of the field.

The even odds starting point is completely unrealistic in terms of the events upon which the parties are wagering. The ASCOT-LLA study might have shown a protective association between atorvastatin and diabetes, or it might have shown no association at all, or it might have show a larger hazard ratio than measured in this particular sample. Recall that the confidence interval for hazard ratios for diabetes ran from 0.91 to 1.44. In other words, parameters from 0.91 (protective association) to 1.0 (no association), to 1.44 (harmful association) were all reasonably compatible with the observed statistic, based upon this one study’s data. The potential outcomes are not binary, which makes the even odds starting point inappropriate.[20]

[1] “Schauer’s Long Footnote on Statistical Significance” (Aug. 21, 2022).

[2] Frederick Schauer, The Proof: Uses of Evidence in Law, Politics, and Everything Else 54-55 (2022) (citing Michelle M. Burtis, Jonah B. Gelbach, and Bruce H. Kobayashi, “Error Costs, Legal Standards of Proof, and Statistical Significance,” 25 Supreme Court Economic Rev. 1 (2017).

[3] In re Lipitor Mktg., Sales Practices & Prods. Liab. Litig., MDL No. 2:14–mn–02502–RMG, 2015 WL 6941132, at *1  (D.S.C. Oct. 22, 2015).

[4] Peter S. Sever, et al., “Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial,” 361 Lancet 1149 (2003). [cited here as ASCOT-LLA]

[5] ASCOT-LLA at 1153 & Table 3.

[6][6] In re Lipitor Mktg., Sales Practices & Prods. Liab. Litig., 174 F.Supp. 3d 911, 921 (D.S.C. 2016) (quoting Dr. Singh’s testimony).

[7] In re Lipitor Mktg., Sales Practices & Prods. Liab. Litig., 892 F.3d 624, 638-39 (2018) (affirming MDL trial court’s exclusion in part of Dr. Singh).

[8] See “Expert Witness Mining – Antic Proposals for Reform” (Nov. 4, 2014).

[9] Brief for Amicus Curiae Jonah B. Gelbach in Support of Plaintiffs-Appellants, In re Lipitor Mktg., Sales Practices & Prods. Liab. Litig., 2017 WL 1628475 (April 28, 2017). [Cited as Gelbach]

[10] Gelbach at *2.

[11] Kumho Tire Co. v. Carmichael, 526 U.S. 137, 152 (1999).

[12] Gelbach at *5.

[13] Gelbach at *2, *6.

[14] Gelbach at *15.

[15] Gelbach at *19-20.

[16] See Richard A. Posner, “An Economic Approach to the Law of Evidence,” 51 Stanford L. Rev. 1477, 1514 (1999) (asserting that the “unbiased fact-finder” should start hearing a case with even odds; “[I]deally we want the trier of fact to work from prior odds of 1 to 1 that the plaintiff or prosecutor has a meritorious case. A substantial departure from this position, in either direction, marks the trier of fact as biased.”).

[17] See, e.g., Richard D. Friedman, “A Presumption of Innocence, Not of Even Odds,” 52 Stan. L. Rev. 874 (2000). [Friedman]

[18] Leonard R. Jaffee, “Prior Probability – A Black Hole in the Mathematician’s View of the Sufficiency and Weight of Evidence,” 9 Cardozo L. Rev. 967, 986 (1988).

[19] Id. at p.994 & n.35.

[20] Friedman at 877.

Posted in Causation, Evidence, Expert Witnesses, Rule 702, Scientific Evidence, statistical evidence | No Comments »

The Rise of Agnothology as Conspiracy Theory

July 19th, 2022

A few egregious articles in the biomedical literature have begun to endorse explicitly asymmetrical standards for inferring causation in the context of environmental or occupational exposures. Very little if anything is needed for inferring causation, and nothing counts against causation.  If authors refuse to infer causation, then they are agents of “industry,” epidemiologic malfeasors, and doubt mongers.

For an example of this genre, take the recent article, entitled “Toolkit for detecting misused epidemiological methods.”[1] [Toolkit] Please.

The asymmetry begins with Trump-like projection of the authors’ own foibles. The principal hammer in the authors’ toolkit for detecting misused epidemiologic methods is personal, financial bias. And yet, somehow, in an article that calls out other scientists for having received money from “industry,” the authors overlooked the business of disclosing their receipt of monies from one of the biggest industries around – the lawsuit industry.

Under the heading “competing interests,” the authors state that “they have no competing interests.”[2]  Lead author, Colin L. Soskolne, was, however, an active, partisan expert witness for plaintiffs’ counsel in diacetyl litigation.[3] In an asbestos case before the Pennsylvania Supreme Court, Rost v. Ford Motor Co., Soskolne signed on to an amicus brief, supporting the plaintiff, using his science credentials, without disclosing his expert witness work for plaintiffs, or his long-standing anti-asbestos advocacy.[4]

Author Shira Kramer signed on to Toolkit, without disclosing any conflicts, but with an even more impressive résumé of pro-plaintiff litigation experience.[5] Kramer is the owner of Epidemiology International, in Cockeysville, Maryland, where she services the lawsuit industry. She too was an “amicus” in Rost, without disclosing her extensive plaintiff-side litigation consulting and testifying.

Carl Cranor, another author of Toolkit, takes first place for hypocrisy on conflicts of interest. As a founder of Council for Education and Research on Toxics (CERT), he has sterling credentials for monetizing the bounty hunt against “carcinogens,” most recently against coffee.[6] He has testified in denture cream and benzene litigation, for plaintiffs. When he was excluded under Rule 702 from the Milward case, CERT filed an amicus brief on his behalf, without disclosing that Cranor was a founder of that organization.[7], [8]

The title seems reasonably fair-minded but the virulent bias of the authors is soon revealed. The Toolkit is presented as a Table in the middle of the article, but the actual “tools” are for the most part not seriously discussed, other than advice to “follow the money” to identify financial conflicts of interest.

The authors acknowledge that epidemiology provides critical knowledge of risk factors and causation of disease, but they quickly transition to an effort to silence any industry commentator on any specific epidemiologic issue. As we will see, the lawsuit industry is given a complete pass. Not surprisingly, several of the authors (Kramer, Cranor, Soskolne) have worked closely in tandem with the lawsuit industry, and have derived financial rewards for their efforts.

Repeatedly, the authors tell us that epidemiologic methods and language are misused by “powerful interests,” which have financial stakes in the outcome of research. Agents of these interests foment uncertainty and doubt about causal relationships through “disinformation,” “malfeasance,” and “doubt mongering.” There is no correlative concern about false claiming or claim mongering..

Who are these agents who plot to sabotage “social justice” and “truth”? Clearly, they are scientists with whom the Toolkit authors disagree. The Toolkit gang cites several papers as exemplifying “malfeasance,”[9] but they never explain what was wrong with them, or how the malfeasors went astray.  The Toolkit tactics seem worthy of Twitter smear and run.

The Toolkit

The authors’ chart of “tools” used by industry might have been an interesting taxonomy of error, but mostly they are ad hominem attack on scientists with whom they disagree. Channeling Putin on Ukraine, those scientists who would impose discipline and rigor on epidemiologic science are derided as not “real epidemiologists,” and, to boot, they are guilty of ethical lapses in failing to advance “social justice.”

Mostly the authors give us a toolkit for silencing those who would get in the way of the situational science deployed at the beck and call of the lawsuit industry.[10] Indeed, the Toolkit authors are not shy about identifying their litigation goals; they tell us that the toolkit can be deployed in depositions and in cross-examinations to pursue “social justice.” These authors also outline a social agenda that greatly resembles the goals of cancel culture: expose the perpetrators who stand in the way of the authors’preferred policy choices, diminish their adversaries’ their influence on journals, and galvanize peer reviewers to reject their adversaries’ scientific publications. The Toolkit authors tell us that “[t] he scientific community should engage by recognizing and professionally calling out common practices used to distort and misapply epidemiological and other health-related sciences.”[11] What this advice translates into are covert and open ad hominem campaigns as peer reviewers to block publications, to deny adversaries tenure and promotions, and to use social and other media outlets to attack adversaries’ motives, good faith, and competence.

None of this is really new. Twenty-five years ago, the late F. Douglas K. Liddell railed at the Mt. Sinai mob, and the phenomenon was hardly new then.[12] The Toolkit’s call to arms is, however, quite open, and raises the question whether its authors and adherents can be fair journal editors and peer reviewers of journal submissions.

Much of the Toolkit is the implementation of a strategy developed by lawsuit industry expert witnesses to demonize their adversaries by accusing them of manufacturing doubt or ignorance or uncertainty. This strategy has gained a label used to deride those who disagree with litigation overclaiming: agnotology or the creation of ignorance. According to Professor Robert Proctor, a regular testifying historian for tobacco plaintiffs, a linguist, Iain Boal, coined the term agnotology, in 1992, to describe the study of the production of ignorance.[13]

The Rise of “Agnotology” in Ngram

Agnotology has become a cottage sub-industry of the lawsuit industry, although lawsuits (or claim mongering if you like), of course, remain their main product. Naomi Oreskes[14] and David Michaels[15] gave the agnotology field greater visibility with their publications, using the less erudite but catchier phrase “manufacturing doubt.” Although the study of ignorance and uncertainty has a legitimate role in epistemology[16] and sociology,[17] much of the current literature is dominated by those who use agnotology as propaganda in support of their own litigation and regulatory agendas.[18] One lone author, however, appears to have taken agnotology study seriously enough to see that it is largely a conspiracy theory that reduces complex historical or scientific theory, evidence, opinion, and conclusions to a clash between truth and a demonic ideology.[19]

Is there any substance to the Toolkit?

The Toolkit is not entirely empty of substantive issues. The authors note that “statistical methods are a critical component of the epidemiologist’s toolkit,”[20] and they cite some articles about common statistical mistakes missed by peer reviewers. Curiously, the Toolkit omits any meaningful discussion of statistical mistakes that increase the risk of false positive results, such as multiple comparisons or dichotomizing continuous confounder variables. As for the Toolkit’s number one identified “inappropriate” technique used by its authors’ adversaries, we have:

“A1. Relying on statistical hypothesis testing; Using ‘statistical significance’ at the 0.05 level of probability as a strict decision criterion to determine the interpretation of statistical results and drawing conclusions.”

Peer into the hearings of any federal court so-called Daubert motion, and you will see the lawsuit industry, and its hired expert witnesses, rail at statistical significance, unless of course, there is some subgroup that has nominal significance, in which case, they are all in for endorsing the finding as “conclusive.” 

Welcome to asymmetric, situational science.

[1] Colin L. Soskolne, Shira Kramer, Juan Pablo Ramos-Bonilla, Daniele Mandrioli, Jennifer Sass, Michael Gochfeld, Carl F. Cranor, Shailesh Advani & Lisa A. Bero, “Toolkit for detecting misused epidemiological methods,” 20(90) Envt’l Health (2021) [Toolkit].

[2] Toolkit at 12.

[3] Watson v. Dillon Co., 797 F.Supp. 2d 1138 (D. Colo. 2011).

[4] Rost v. Ford Motor Co., 151 A.3d 1032 (Pa. 2016). See “The Amicus Curious Brief” (Jan. 4, 2018).

[5] See, e.g., Sean v. BMW of North Am., LLC, 26 N.Y.3d 801, 48 N.E.3d 937, 28 N.Y.S.3d 656 (2016) (affirming exclusion of Kramer); The Little Hocking Water Ass’n v. E.I. Du Pont De Nemours & Co., 90 F.Supp.3d 746 (S.D. Ohio 2015) (excluding Kramer); Luther v. John W. Stone Oil Distributor, LLC, No. 14-30891 (5th Cir. April 30, 2015) (mentioning Kramer as litigation consultant); Clair v. Monsanto Co., 412 S.W.3d 295 (Mo. Ct. App. 2013 (mentioning Kramer as plaintiffs’ expert witness); In re Chantix (Varenicline) Prods. Liab. Litig., No. 2:09-CV-2039-IPJ, MDL No. 2092, 2012 WL 3871562 (N.D.Ala. 2012) (excluding Kramer’s opinions in part); Frischhertz v. SmithKline Beecham Corp., 2012 U.S. Dist. LEXIS 181507, Civ. No. 10-2125 (E.D. La. Dec. 21, 2012) (excluding Kramer); Donaldson v. Central Illinois Public Service Co., 199 Ill. 2d 63, 767 N.E.2d 314 (2002) (affirming admissibility of Kramer’s opinions in absence of Rule 702 standards).

[6]  “The Council for Education & Research on Toxics” (July 9, 2013) (CERT amicus brief filed without any disclosure of conflict of interest). Among the fellow travelers who wittingly or unwittingly supported CERT’s scheme to pervert the course of justice were lawsuit industry stalwarts, Arthur L. Frank, Peter F. Infante, Philip J. Landrigan, Barry S. Levy, Ronald L. Melnick, David Ozonoff, and David Rosner. See also NAS, “Carl Cranor’s Conflicted Jeremiad Against Daubert” (Sept. 23, 2018); Carl Cranor, “Milward v. Acuity Specialty Products: How the First Circuit Opened Courthouse Doors for Wronged Parties to Present Wider Range of Scientific Evidence” (July 25, 2011).

[7] Milward v. Acuity Specialty Products Group, Inc., 664 F. Supp. 2d 137, 148 (D. Mass. 2009), rev’d, 639 F.3d 11 (1st Cir. 2011), cert. den. sub nom. U.S. Steel Corp. v. Milward, 565 U.S. 1111 (2012), on remand, Milward v. Acuity Specialty Products Group, Inc., 969 F.Supp. 2d 101 (D. Mass. 2013) (excluding specific causation opinions as invalid; granting summary judgment), aff’d, 820 F.3d 469 (1st Cir. 2016).

[8] To put this effort into a sociology of science perspective, the Toolkit article is published in a journal, Environmental Health, an Editor in Chief of which is David Ozonoff, a long-time pro-plaintiff partisan in the asbestos litigation. The journal has an “ombudsman,”Anthony Robbins, who was one of the movers-and-shakers in forming SKAPP, The Project on Scientific Knowledge and Public Policy, a group that plotted to undermine the application of federal evidence law of expert witness opinion testimony. SKAPP itself now defunct, but its spirit of subverting law lives on with efforts such as the Toolkit. “More Antic Proposals for Expert Witness Testimony – Including My Own Antic Proposals” (Dec. 30, 2014). Robbins is also affiliated with an effort, led by historian and plaintiffs’ expert witness David Rosner, to perpetuate misleading historical narratives of environmental and occupational health. “ToxicHistorians Sponsor ToxicDocs” (Feb. 1, 2018); “Creators of ToxicDocs Show Off Their Biases” (June 7, 2019); Anthony Robbins & Phyllis Freeman, “ToxicDocs (www.ToxicDocs.org) goes live: A giant step toward leveling the playing field for efforts to combat toxic exposures,” 39 J. Public Health Pol’y 1 (2018).

[9] The exemplars cited were Paolo Boffetta, MD, MPH; Hans Olov Adami, Philip Cole, Dimitrios Trichopoulos, Jack Mandel, “Epidemiologic studies of styrene and cancer: a review of the literature,” 51 J. Occup. & Envt’l Med. 1275 (2009); Carlo LaVecchia & Paolo Boffetta, “Role of stopping exposure and recent exposure to asbestos in the risk of mesothelioma,” 21 Eur. J. Cancer Prev. 227 (2012); John Acquavella, David Garabrant, Gary Marsh G, Thomas Sorahan and Douglas L. Weed, “Glyphosate epidemiology expert panel review: a weight of evidence systematic review of the relationship between glyphosate exposure and non-Hodgkin’s lymphoma or multiple myeloma,” 46 Crit. Rev. Toxicol. S28 (2016); Catalina Ciocan, Nicolò Franco, Enrico Pira, Ihab Mansour, Alessandro Godono, and Paolo Boffetta, “Methodological issues in descriptive environmental epidemiology. The example of study Sentieri,” 112 La Medicina del Lavoro 15 (2021).

[10] The Toolkit authors acknowledge that their identification of “tools” was drawn from previous publications of the same ilk, in the same journal. Rebecca F. Goldberg & Laura N. Vandenberg, “The science of spin: targeted strategies to manufacture doubt with detrimental effects on environmental and public health,” 20:33 Envt’l Health (2021).

[11] Toolkit at 11.

[12] F.D.K. Liddell, “Magic, Menace, Myth and Malice,” 41 Ann. Occup. Hyg. 3, 3 (1997). See “The Lobby – Cut on the Bias” (July 6, 2020).

[13] Robert N. Proctor & Londa Schiebinger, Agnotology: The Making and Unmaking of Ignorance (2008).

[14] Naomi Oreskes & Erik M. Conway, Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming (2010); Naomi Oreskes & Erik M. Conway, “Defeating the merchants of doubt,” 465 Nature 686 (2010).

[15] David Michaels, The Triumph of Doubt: Dark Money and the Science of Deception (2020); David Michaels, Doubt is Their Product: How Industry’s Assault on Science Threatens Your Health (2008); David Michaels, “Science for Sale,” Boston Rev. 2020; David Michaels, “Corporate Campaigns Manufacture Scientific Doubt,” 174 Science News 32 (2008); David Michaels, “Manufactured Uncertainty: Protecting Public Health in the Age of Contested Science and Product Defense,” 1076 Ann. N.Y. Acad. Sci. 149 (2006); David Michaels, “Scientific Evidence and Public Policy,” 95 Am. J. Public Health s1 (2005); David Michaels & Celeste Monforton, “Manufacturing Uncertainty: Contested Science and the Protection of the Public’s Health and Environment,” 95 Am. J. Pub. Health S39 (2005); David Michaels & Celeste Monforton, “Scientific Evidence in the Regulatory System: Manufacturing Uncertainty and the Demise of the Formal Regulatory Ssytem,” 13 J. L. & Policy 17 (2005); David Michaels, “Doubt is Their Product,” Sci. Am. 96 (June 2005); David Michaels, “The Art of ‘Manufacturing Uncertainty’,” L.A. Times (June 24, 2005).

[16] See, e.g., Sibilla Cantarini, Werner Abraham, and Elisabeth Leiss, eds., Certainty-uncertainty – and the Attitudinal Space in Between (2014); Roger M. Cooke, Experts in Uncertainty: Opinion and Subjective Probability in Science (1991).

[17] See, e.g., Ralph Hertwig & Christoph Engel, eds., Deliberate Ignorance: Choosing Not to Know (2021); Linsey McGoey, The Unknowers: How Strategic Ignorance Rules the World (2019); Michael Smithson, “Toward a Social Theory of Ignorance,” 15 J. Theory Social Behavior 151 (1985).

[18] See Janet Kourany & Martin Carrier, eds., Science and the Production of Ignorance: When the Quest for Knowledge Is Thwarted (2020); John Launer, “The production of ignorance,” 96 Postgraduate Med. J. 179 (2020); David S. Egilman, “The Production of Corporate Research to Manufacture Doubt About the Health Hazards of Products: An Overview of the Exponent BakeliteVR Simulation Study,” 28 New Solutions 179 (2018); Larry Dossey, “Agnotology: on the varieties of ignorance, criminal negligence, and crimes against humanity,” 10 Explore 331 (2014); Gerald Markowitz & David Rosner, Deceit and Denial: The Deadly Politics of Industrial Revolution (2002).

[19] See Enea Bianchi, “Agnotology: a Conspiracy Theory of Ignorance?” Ágalma: Rivista di studi culturali e di estetica 41 (2021).

[20] Toolkit at 4.

Posted in Causation, Cognitive Biases, Conflicts of Interest, Public Nuisances, Scientific Evidence, Scientific Misconduct, Scientific Publishing, statistical evidence | No Comments »

Madigan’s Shenanigans & Wells Quelled in Incretin-Mimetic Cases

July 15th, 2022

The incretin-mimetic litigation involved claims that the use of Byetta, Januvia, Janumet, and Victoza medications causes pancreatic cancer. All four medications treat diabetes mellitus through incretin hormones, which stimulate or support insulin production, which in turn lowers blood sugar. On Planet Earth, the only scientists who contend that these medications cause pancreatic cancer are those hired by the lawsuit industry.

The cases against the manufacturers of the incretin-mimetic medications were consolidated for pre-trial proceedings in federal court, pursuant to the multi-district litigation (MDL) statute, 28 US Code § 1407. After years of MDL proceedings, the trial court dismissed the cases as barred by the doctrine of federal preemption, and for good measure, excluded plaintiffs’ medical causation expert witnesses from testifying.[1] If there were any doubt about the false claiming in this MDL, the district court’s dismissals were affirmed by the Ninth Circuit.[2]

The district court’s application of Federal Rule of Evidence 702 to the plaintiffs’ expert witnesses’ opinion is an important essay in patho-epistemology. The challenged expert witnesses provided many examples of invalid study design and interpretation. Of particular interest, two of the plaintiffs’ high-volume statistician testifiers, David Madigan and Martin Wells, proffered their own meta-analyses of clinical trial safety data. Although the current edition of the Reference Manual on Scientific Evidence[3] provides virtually no guidance to judges for assessing the validity of meta-analyses, judges and counsel do now have other readily available sources, such as the FDA’s Guidance on meta-analysis of safety outcomes of clinical trials.[4] Luckily for the Incretin-Mimetics pancreatic cancer MDL judge, the misuse of meta-analysis methodology by plaintiffs’ statistician expert witnesses, David Madigan and Martin Wells was intuitively obvious.

Madigan and Wells had a large set of clinical trials at their disposal, with adverse safety outcomes assiduously collected. As is the case with many clinical trial safety outcomes, the trialists will often have a procedure for blinded or unblinded adjudication of safety events, such as pancreatic cancer diagnosis.

At deposition, Madigan testified that he counted only adjudicated cases of pancreatic cancer in his meta-analyses, which seems reasonable enough. As discovery revealed, however, Madigan employed the restrictive inclusion criteria of adjudicated pancreatic cancer only to the placebo group, not to the experimental group. His use of restrictive inclusion criteria for only the placebo group had the effect of excluding several non-adjudicated events, with the obvious spurious inflation of risk ratios. The MDL court thus found with relative ease that Madigan’s “unequal application of criteria among the two groups inevitably skews the data and critically undermines the reliability of his analysis.” The unexplained, unjustified change in methodology revealed Madigan’s unreliable “cherry-picking” and lack of scientific rigor as producing a result-driven meta-analyses.[5]

The MDL court similarly found that Wells’ reports “were marred by a selective review of data and inconsistent application of inclusion criteria.”[6] Like Madigan, Wells cherry picked studies. For instance, he excluded one study, EXSCEL, on grounds that it reported “a high pancreatic cancer event rate in the comparison group as compared to background rate in the general population….”[7] Wells’ explanation blatantly failed, however, given that the entire patient population of the clinical trial had diabetes, a known risk factor for pancreatic cancer.[8]

As Professor Ioannidis and others have noted, we are awash in misleading meta-analyses:

“Currently, there is massive production of unnecessary, misleading, and conflicted systematic reviews and meta-analyses. Instead of promoting evidence-based medicine and health care, these instruments often serve mostly as easily produced publishable units or marketing tools.  Suboptimal systematic reviews and meta-analyses can be harmful given the major prestige and influence these types of studies have acquired.  The publication of systematic reviews and meta-analyses should be realigned to remove biases and vested interests and to integrate them better with the primary production of evidence.”[9]

Whether created for litigation, like the Madigan-Wells meta-analyses, or published in the “peer-reviewed” literature, courts will have to up their game in assessing the validity of such studies. Published meta-analyses have grown exponentially from the 1990s to the present. To date, 248,886 meta-analyses have been published, according the National Library of Medicine’s Pub-Med database. Last year saw over 35,000 meta-analyses published. So far, this year, 20,416 meta-analyses have been published, and we appear to be on track to have a bumper crop.

The data analytics from Pub-Med provide a helpful visual representation of the growth of meta-analyses in biomedical science.

 

Count of Publications with Keyword Meta-analysis in Pub-Med Database

In 1979, the year I started law school, one meta-analysis was published. Lawyers could still legitimately argue that meta-analyses involved novel methodology that had not been generally accepted. The novelty of meta-analysis wore off sometime between 1988, when Judge Robert Kelly excluded William Nicholson’s meta-analysis of health outcomes among PCB-exposed workers, on grounds that such analyses were “novel,” and 1990, when the Third Circuit reversed Judge Kelly, with instructions to assess study validity.[10] Fortunately, or not, depending upon your point of view, plaintiffs dropped Nicholson’s meta-analysis in subsequent proceedings. A close look at Nicholson’s non-peer reviewed calculations shows that he failed to standardize for age or sex, and that he merely added observed and expected cases, across studies, without weighting by individual study variance. The trial court never had the opportunity to assess the validity vel non of Nicholson’s ersatz meta-analysis.[11] Today, trial courts must pick up on the challenge of assessing study validity of meta-analyses relied upon by expert witnesses, regulatory agencies, and systematic reviews.

[1] In re Incretin-Based Therapies Prods. Liab. Litig., 524 F.Supp.3d 1007 (S.D. Cal. 2021).

[2] In re Incretin-Based Therapies Prods. Liab. Litig., No. 21-55342, 2022 WL 898595 (9th Cir. Mar. 28, 2022) (per curiam)

[3]  “The Treatment of Meta-Analysis in the Third Edition of the Reference Manual on Scientific Evidence” (Nov. 15, 2011).

[4] Food and Drug Administration, Center for Drug Evaluation and Research, “Meta-Analyses of Randomized Controlled Clinical Trials to Evaluate the Safety of Human Drugs or Biological Products – (Draft) Guidance for Industry” (Nov. 2018); Jonathan J. Deeks, Julian P.T. Higgins, Douglas G. Altman, “Analysing data and undertaking meta-analyses,” Chapter 10, in Julian P.T. Higgins, James Thomas, Jacqueline Chandler, Miranda Cumpston, Tianjing Li, Matthew J. Page, and Vivian Welch, eds., Cochrane Handbook for Systematic Reviews of Interventions (version 6.3 updated February 2022); Donna F. Stroup, Jesse A. Berlin, Sally C. Morton, Ingram Olkin, G. David Williamson, Drummond Rennie, David Moher, Betsy J. Becker, Theresa Ann Sipe, Stephen B. Thacker, “Meta-Analysis of Observational Studies: A Proposal for Reporting,” 283 J. Am. Med. Ass’n 2008 (2000); David Moher, Alessandro Liberati, Jennifer Tetzlaff, and Douglas G Altman, “Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement,” 6 PLoS Med e1000097 (2009).

[5] In re Incretin-Based Therapies Prods. Liab. Litig., 524 F.Supp.3d 1007, 1037 (S.D. Cal. 2021). See In re Lipitor (Atorvastatin Calcium) Mktg., Sales Practices & Prods. Liab. Litig. (No. II) MDL2502, 892 F.3d 624, 634 (4th Cir. 2018) (“Result-driven analysis, or cherry-picking, undermines principles of the scientific method and is a quintessential example of applying methodologies (valid or otherwise) in an unreliable fashion.”).

[6] In re Incretin-Based Therapies Prods. Liab. Litig., 524 F.Supp.3d 1007, 1043 (S.D. Cal. 2021).

[7] Id. at 1038.

[8] See, e.g., Albert B. Lowenfels & Patrick Maisonneuve, “Risk factors for pancreatic cancer,” 95 J. Cellular Biochem. 649 (2005).

[9] John P. Ioannidis, “The mass production of redundant, misleading, and conflicted systematic reviews and meta-analyses,” 94 Milbank Quarterly 485 (2016).

[10] In re Paoli R.R. Yard PCB Litig., 706 F. Supp. 358, 373 (E.D. Pa. 1988), rev’d and remanded, 916 F.2d 829, 856-57 (3d Cir. 1990), cert. denied, 499 U.S. 961 (1991). See also Hines v. Consol. Rail Corp., 926 F.2d 262, 273 (3d Cir. 1991).

[11] “The Shmeta-Analysis in Paoli” (July 11, 2019). See  James A. Hanley, Gilles Thériault, Ralf Reintjes and Annette de Boer, “Simpson’s Paradox in Meta-Analysis,” 11 Epidemiology 613 (2000); H. James Norton & George Divine, “Simpson’s paradox and how to avoid it,” Significance 40 (Aug. 2015); George Udny Yule, “Notes on the theory of association of attributes in statistics,” 2 Biometrika 121 (1903).

Posted in Causation, Expert Witness Hall of Shame, Meta-analysis, Rule 702, Scientific Evidence, Scientific Misconduct, statistical evidence | 1 Comment »

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).

Posted in Causation, Rule 702, statistical evidence | No Comments »

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).

Posted in Causation | 45 Comments »

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.

Posted in Causation | No Comments »

« Older Entries
Newer Entries »