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

Passing Hypotheses Off as Causal Conclusions – Allen v. Martin Surfacing

November 11th, 2018

The November 2018 issue of the American Bar Association Journal (ABAJ) featured an exposé-style article on the hazards of our chemical environment, worthy of Mother Jones, or the International Journal of Health Nostrums, by a lawyer, Alan Bell.1 Alan Bell, according to his website, is a self-described “environmental health warrior.” Channeling Chuck McGill, Bell also describes himself as a:

[v]ictim, survivor, advocate and avenger. This former organized crime prosecutor almost died from an environmentally linked illness. He now devotes his life to giving a voice for those too weak or sick to fight for themselves.”

Bell apparently is not so ill that he cannot also serve as “a fierce advocate” for victims of chemicals. Here is how Mr. Bell described his own “environmentally linked illness” (emphasis added):

Over the following months, Alan developed high fevers, sore throats, swollen glands and impaired breathing. Eventually, he experienced seizures and could barely walk. His health continued to worsen until he became so ill he was forced to stop working. Despite being examined and tested by numerous world-renowned doctors, none of them could help. Finally, a doctor diagnosed him with Multiple Chemical Sensitivity, a devastating illness caused by exposure to environmental toxins. The medical profession had no treatment to offer Alan: no cure, and no hope. Doctors could only advise him to avoid all synthetic chemicals and live in complete isolation within a totally organic environment.”

Multiple chemical sensitivity (MCS)? Does anyone still remember “clinical ecology”? Despite the strident advocacy of support groups and self-proclaimed victims, MCS is not recognized as a chemically caused illness by the World Health Organization, the American Medical Association, the American Academy of Allergy and Immunology, and the American College of Physicians.2 Double-blinded, placebo-controlled clinical trials have shown that putative MCS patients respond to placebo as strongly as they react to chemicals.3

Still, Bell’s claims must be true; Bell has written a book, Poisoned, about his ordeal and that of others.4 After recounting his bizarre medical symptoms, he describes his miraculous cure in a sterile bubble in the Arizona desert. From safe within his bubble, Bell has managed to create the “Environmental Health Foundation,” which is difficult if not impossible to find on the internet, although there are some cheesy endorsements to be found on YouTube.

According to Bell’s narrative, Daniel Allen, the football coach of the College of the Holy Cross was experiencing neurological signs and symptoms that could not be explained by physicians in the Boston area, home to some of the greatest teaching hospitals in the world. Allen and his wife, Laura, reached out Bell through his Foundation. Bell describes how he put the Allens in touch with Marcia Ratner, who sits on the Scientific Advisory Board of his Environmental Health Foundation. Bell sent the Allens to see “the world renown” Marcia Ratner, who diagnosed Mr. Allen with amyotrophic lateral sclerosis (ALS). Bell’s story may strike some as odd, considering that Ratner is not a physician. Ratner could not provide a cure for Mr. Allen’s tragic disease, but she could help provide the Allens with a lawsuit.

According to Bell:

Testimony from a sympathetic widow, combined with powerful evidence that the chemicals Dan was exposed to caused him to die long before his time, would smash their case to bits. The defense opted to seek a settlement. The case settled in 2009.5

The ABAJ article on the Allen case is a reprise of chapter 15 of Bell’s book “Chemicals Take Down a Football Coach.” Shame on the A.B.A. for not marking the article as unpaid advertising. More shame on the A.B.A. for not fact checking the glib causal claims made in the article, some of which have been the subject of a recently published “case report” in the red journal, the American Journal of Industrial Medicine, by Dr. Ratner and some, but not all, of the other expert witnesses for Mr. Allen’s litigation team.6 Had the editors of the ABAJ compared Mr. Bell’s statements and claims about the Allen case, they would have seen that Dr. Ratner, et al., ten years after beating back the defendants’ Daubert motion in the Allen case, described their literature review and assessment of Mr. Allen’s case, as merely “hypothesis generating”:

This literature review and clinical case report about a 45-year-old man with no family history of motor neuron disease who developed overt symptoms of a neuromuscular disorder in close temporal association with his unwitting occupational exposure to volatile organic compounds (VOCs) puts forth the hypothesis that exposure to VOCs such as toluene, which disrupt motor function and increase oxidative stress, can unmask latent ALS type neuromuscular disorder in susceptible individuals.”7

         * * * * * * *

In conclusion, this hypothesis generating case report provides additional support for the suggestion that exposure to chemicals that share common mechanisms of action with those implicated in the pathogenesis of ALS type neuromuscular disorders can unmask latent disease in susceptible persons. Further research is needed to elucidate these relationships.”8

So in 2018, the Allen case was merely a “hypothesis generating” case report. Ten years earlier, however, in 2008, when Ratner, Abou-Donia, Oliver, Ewing, and Clapp gave solemn oaths and testified under penalty of perjury to a federal district judge, the facts of the same case warranted a claim to scientific knowledge, under Rule 702. Judges, lawyers, and legal reformers should take note of how expert witnesses will characterize facile opinions as causal conclusions when speaking as paid witnesses, and as mere hypotheses in need of evidentiary support when speaking in professional journals to scientists. You’re shocked; eh?

Sometimes when federal courts permit dubious causation opinion testimony over Rule 702 objections, the culprit is bad lawyering by the opponent of the proffered testimony. The published case report by Ratner helps demonstrate that Allen v. Martin Surfacing, 263 F.R.D. 47 (D. Mass. 2009), was the result of litigation overreach by plaintiffs’ counsel and their paid expert witnesses, and a failure of organized skepticism by defense counsel and the judiciary.

Marcia H. Ratner, Ph.D.

I first encountered Dr. Ratner as an expert witness for the litigation industry in cases involving manganese-containing welding rods. Plaintiffs’ counsel, Dickie Scruggs, et al., withdrew her before the defense could conduct an examination before trial. When I came across the Daubert decision in the Allen case, I was intrigued because I had read Ratner’s dissertation9 and her welding litigation report, and saw what appeared to be fallacies10 similar to those that plagued the research of Dr. Brad Racette, who also had worked with Scruggs in conducting screenings, from which he extracted “data” for a study, which for a while became the center piece of Scruggs’ claims.11

The Allen case provoked some research on my part, and then a blog post about that case and Dr. Ratner.12 Dr. Ratner took umbrage to my blog post; and in email correspondence, she threatened to sue me for tortious interference with her prospective business opportunities. She also felt that the blog post had put her in a bad light by commenting upon her criminal conviction for unlawful gun possession.13 As a result of our correspondence, and seeing that Dr. Ratner was no stranger to the courtroom,14 I wrote a post-script to add some context and her perspective on my original post.15

One fact Dr Ratner wished me to include in the blog post-script was that plaintiffs’ counsel in the Allen case had pressured her to opine that toluene and isocyanates caused Mr. Allen’s ALS, and that she had refused. Dr. Ratner of course was making a virtue of necessity since there was, and is, a mountain of medical opinion, authoritative and well-supportive, that there is no known cause of sporadic ALS.16 Dr. Ratner was very proud, however, of having devised a work-around, by proffering an opinion that toluene caused the acceleration of Mr. Allen’s ALS. This causal claim about accelerated onset could have been tested with an observational study, but the litigation claim about earlier onset was as lacking in evidential support as the more straightforward claim of causation.

Bell’s article in the ABAJ – or rather his advertisement17 – cited an unpublished write up of the Allen case, by Ratner, The Allen Case: Our Daubert Strategy, Victory, and Its Legal and Medical Landmark Ramifications, in which she kvelled about how the Allen case was cited in the Reference Manual on Scientific Evidence. The Manual’s citations, however, were about the admissibility of the industrial hygienist’s proffered testimony on exposure, based in turn on Mr. Allen’s account of acute-onset symptoms.18 The Manual does not address the dubious acceleration aspect of Ratner’s causal opinion in the Allen case.

The puff piece in the ABAJ caused me to look again at Dr. Ratner’s activities. According to the Better Business Bureau reports that Dr. Marcia Ratner is a medical consultant in occupational and environmental toxicology. Since early 2016, she has been the sole proprietor of a consulting firm,, located in Mendon, Vermont. The firm’s website advertises that:

The Principals and Consultants of provide expert consulting in neurotoxicology and the relationships between neurotoxic chemical exposures and neurodegenerative disease onset and progression.

Only Ratner is identified as working on consulting through the firm. According to the LinkedIn entry for, Ratner is the also founder and director of Medical-Legal Research at Ratner’s website advertises her involvement in occupational exposure litigation as an expert witness for claimants.19 Previously, Ratner was the Vice President and Director of Research at Chemical Safety Net, Inc., another consulting firm that she had founded with the late Robert G. Feldman, MD.

Conflict of Interest

The authors of the published Allen case report gave a curious conflict-of-interest disclosure at the end of their article:

The authors have no current specific competing interests to declare. However, Drs. Ratner, Abou-Donia and Oliver, and Mr. Ewing all served as expert witnesses in this case which settled favorably for the patient over 10 years ago with an outcome that is a fully disclosed matter of public record. Drs. Ratner, Abou-Donia and Oliver and Mr. Ewing are occasionally asked to serve as expert witnesses and/or consultants in occupational and environmental chemical exposure injury cases.”20

The disclosure conveniently omitted that Dr. Ratner owns a business that she set up to provide medico-legal consulting, and that Dr. Oliver testifies with some frequency in asbestos cases. None of the authors was, or is, an expert in the neuroepidemiology of ALS. Dr. Ratner’s conflict-of-interest disclosure in the Allen case report was, however, better than her efforts in previous publications that touched on the subject matter of her commercial consulting practice.21

1 Alan Bell, “Devastated by office chemicals, an attorney helps others fight toxic torts,Am. Bar. Ass’n J. (Nov. 2018).

2 See, e.g., American Academy of Allergy, Asthma and Immunology, “Idiopathic environmental intolerances,” 103 J. Allergy Clin. Immunol. 36 (1999).

3 See Susanne Bornschein, Constanze Hausteiner, Horst Römmelt, Dennis Nowak, Hans Förstl, and Thomas Zilker, “Double-blind placebo-controlled provocation study in patients with subjective Multiple Chemical Sensitivity and matched control subjects,” 46 Clin. Toxicol. 443 (2008); Susanne Bornschein, Hans Förstl, and Thomas Zilker, “Idiopathic environmental intolerances (formerly multiple chemical sensitivity) psychiatric perspectives,” 250 J. Intern. Med. 309 (2001).

4 Poisoned: How a Crime-Busting Prosecutor Turned His Medical Mystery into a Crusade for Environmental Victims (Skyhorse Publishing 2017).

5 Steven H. Foskett Jr., “Late Holy Cross coach’s family, insurers settle lawsuit for $681K,” Telegram & Gazette (Oct. 1, 2009). Obviously, the settlement amount represented a deep compromise over any plaintiff’s verdict.

6 Marcia H. Ratner, Joe F. Jabre, William M. Ewing, Mohamed Abou-Donia, and L. Christine Oliver, “Amyotrophic lateral sclerosis—A case report and mechanistic review of the association with toluene and other volatile organic compounds,” 61 Am. J. Ind. Med. 251 (2018).

7 Id. at 251.

8 Id. at 258 (emphasis added).

9 Marcia Hillary Ratner, Age at Onset of Parkinson’s Disease Among Subjects Occupationally Exposed to Metals and Pesticides; Doctoral Dissertation, UMI Number 3125932, Boston University (2004). Neither Ratner’s dissertation supervisor nor her three readers were epidemiologists.

11 See Brad A. Racette, S.D. Tabbal, D. Jennings, L. Good, Joel S. Perlmutter, and Brad Evanoff, “Prevalence of parkinsonism and relationship to exposure in a large sample of Alabama welders,” 64 Neurology 230 (2005).

13 See Quincy District Court News,” Patriot Ledger June 09, 2010 (reporting that Ratner pleaded guilty to criminal possession of mace and a firearm).

14 Ratner v. Village Square at Pico Condominium Owners Ass’n, Inc., No. 91-2-11 Rdcv (Teachout, J., Aug. 28, 2012).

17 Bell is a client of the Worthy Marketing Group.

18 RMSE3d at 505-06 n.5, 512-13 n. 26, 540 n.88; see also Allen v. Martin Surfacing, 2009 WL 3461145, 2008 U.S. Dist. LEXIS 111658, 263 F.R.D. 47 (D. Mass. 2008) (holding that an industrial hygienist was qualified to testify about the concentration and duration of plaintiffs’ exposure to toluene and isocyanates).

20 Id. at 259. One of the plaintiffs’ expert witnesses, Richard W. Clapp, opted out of co-author status on this publication.

21 See Marcia H. Ratner & Edward Fitzgerald, “Understanding of the role of manganese in parkinsonism and Parkinson disease,” 88 Neurology 338 (2017) (claiming no relevant conflicts of interest); Marcia H. Ratner, David H. Farb, Josef Ozer, Robert G. Feldman, and Raymon Durso, “Younger age at onset of sporadic Parkinson’s disease among subjects occupationally exposed to metals and pesticides,” 7 Interdiscip. Toxicol. 123 (2014) (failing to make any disclosure of conflicts of interest). In one short case report written with Dr. Jonathan Rutchik, another expert witness actively participated for the plaintiffs’ litigation industry in welding fume cases, Dr. Ratner let on that she “occasionally” is asked to serve as an expert witness, but she failed to disclose that she has a business enterprise set up to commercialize her expert witness work. Jonathan Rutchik & Marcia H. Ratner, “Is it Possible for Late-Onset Schizophrenia to Masquerade as Manganese Psychosis?” 60 J. Occup. & Envt’l Med. E207 (2018) (“The authors have no current specific competing interests to declare. However, Dr. Rutchik served as expert witnesses [sic] in this case. Drs. Rutchik and Ratner are occasionally asked to serve as expert witnesses and/or consultants in occupational and environmental chemical exposure injury cases.”)

Confounding in Daubert, and Daubert Confounded

November 4th, 2018


The Daubert trilogy and the statutory revisions to Rule 702 have not brought universal enlightenment. Many decisions reflect a curmudgeonly and dismissive approach to gatekeeping.

The New Jersey Experience

Until recently, New Jersey law looked as though it favored vigorous gatekeeping of invalid expert witness opinion testimony. The law as applied, however, was another matter, with most New Jersey judges keen to find ways to escape the logical and scientific implications of the articulated standards, at least in civil cases.1 For example, in Grassis v. Johns-Manville Corp., 248 N.J. Super. 446, 591 A.2d 671, 675 (App. Div. 1991), the intermediate appellate court discussed the possibility that confounders may lead to an erroneous inference of a causal relationship. Plaintiffs’ counsel claimed that occupational asbestos exposure causes colorectal cancer, but the available studies, inconsistent as they were, failed to assess the role of smoking, family history, and dietary factors. The court essentially shrugged its judicial shoulders and let a plaintiffs’ verdict stand, even though it was supported by expert witness testimony that had relied upon seriously flawed and confounded studies. Not surprisingly, 15 years after the Grassis case, the scientific community acknowledged what should have been obvious in 1991: the studies did not support a conclusion that asbestos causes colorectal cancer.2

This year, however, saw the New Jersey Supreme Court step in to help extricate the lower courts from their gatekeeping doldrums. In a case that involved the dismissal of plaintiffs’ expert witnesses’ testimony in over 2,000 Accutane cases, the New Jersey Supreme Court demonstrated how to close the gate on testimony that is based upon flawed studies and involves tenuous and unreliable inferences.3 There were other remarkable aspects of the Supreme Court’s Accutane decision. For instance, the Court put its weight behind the common-sense and accurate interpretation of Sir Austin Bradford Hill’s famous articulation of factors for causal judgment, which requires that sampling error, bias, and confounding be eliminated before assessing whether the observed association is strong, consistent, plausible, and the like.4

Cook v. Rockwell International

The litigation over radioactive contamination from the Colorado Rocky Flats nuclear weapons plant is illustrative of the retrograde tendency in some federal courts. The defense objected to plaintiffs’ expert witness, Dr. Clapp, whose study failed to account for known confounders.5 Judge Kane denied the challenge, claiming that the defense could:

cite no authority, scientific or legal, that compliance with all, or even one, of these factors is required for Dr. Clapp’s methodology and conclusions to be deemed sufficiently reliable to be admissible under Rule 702. The scientific consensus is, in fact, to the contrary. It identifies Defendants’ list of factors as some of the nine factors or lenses that guide epidemiologists in making judgments about causation. Ref. Guide on Epidemiolog at 375.).”6

In Cook, the trial court or the parties or both missed the obvious references in the Reference Manual to the need to control for confounding. Certainly many other scientific sources could be cited as well. Judge Kane apparently took a defense expert witness’s statement that ecological studies do not account for confounders to mean that the presence of confounding does not render such studies unscientific. Id. True but immaterial. Ecological studies may be “scientific,” but they do not warrant inferences of causation. Some so-called scientific studies are merely hypothesis generating, preliminary, tentative, or data-dredging exercises. Judge Kane employed the flaws-are-features approach, and opined that ecological studies are merely “less probative” than other studies, and the relative weights of studies do not render them inadmissible.7 This approach is, of course, a complete abdication of gatekeeping responsibility. First, studies themselves are not admissible; it is the expert witness, whose testimony is challenged. The witness’s reliance upon studies is relevant to the Rule 702 and 703 analyses, but admissibility is not the issue. Second, Rule 702 requires that the proffered opinion be “scientific knowledge,” and ecological studies simply lack the necessary epistemic warrant to support a causal conclusion. Third, the trial court in Cook had to ignore the federal judiciary’s own reference manual’s warnings about the inability of ecological studies to provide causal inferences.8 The Cook case is part of an unfortunate trend to regard all studies as “flawed,” and their relative weights simply a matter of argument and debate for the litigants.9


Another example of sloppy reasoning about confounding can be found in a recent federal trial court decision, In re Abilify Products Liability Litigation,10 where the trial court advanced a futility analysis. All observational studies have potential confounding, and so confounding is not an error but a feature. Given this simplistic position, it follows that failure to control for every imaginable potential confounder does not invalidate an epidemiologic study.11 From its nihilistic starting point, the trial court readily found that an expert witness could reasonably dispense with controlling for confounding factors of psychiatric conditions in studies of a putative association between the antipsychotic medication Abilify and gambling disorders.12

Under this sort of “reasoning,” some criminal defense lawyers might argue that since all human beings are “flawed,” we have no basis to distinguish sinners from saints. We have a long way to go before our courts are part of the evidence-based world.

1 In the context of a “social justice” issue such as whether race disparities exist in death penalty cases, New Jersey court has carefully considered confounding in its analyses. See In re Proportionality Review Project (II), 165 N.J. 206, 757 A.2d 168 (2000) (noting that bivariate analyses of race and capital sentences were confounded by missing important variables). Unlike the New Jersey courts (until the recent decision in Accutane), the Texas courts were quick to adopt the principles and policies of gatekeeping expert witness opinion testimony. See Merrell Dow Pharms., Inc. v. Havner, 953 S.W.2d 706, 714, 724 (Tex.1997) (reviewing court should consider whether the studies relied upon were scientifically reliable, including consideration of the presence of confounding variables).  Even some so-called Frye jurisdictions “get it.” See, e.g., Porter v. SmithKline Beecham Corp., No. 3516 EDA 2015, 2017 WL 1902905 *6 (Phila. Super., May 8, 2017) (unpublished) (affirming exclusion of plaintiffs’ expert witness on epidemiology, under Frye test, for relying upon an epidemiologic study that failed to exclude confounding as an explanation for a putative association), affirming, Mem. Op., No. 03275, 2015 WL 5970639 (Phila. Ct. Com. Pl. Oct. 5, 2015) (Bernstein, J.), and Op. sur Appellate Issues (Phila. Ct. Com. Pl., Feb. 10, 2016) (Bernstein, J.).

3 In re Accutane Litig., ___ N.J. ___, ___ A.3d ___, 2018 WL 3636867 (2018); see N.J. Supreme Court Uproots Weeds in Garden State’s Law of Expert Witnesses(Aug. 8, 2018).

2018 WL 3636867, at *20 (citing the Reference Manual 3d ed., at 597-99).

5 Cook v. Rockwell Internat’l Corp., 580 F. Supp. 2d 1071, 1098 (D. Colo. 2006) (“Defendants next claim that Dr. Clapp’s study and the conclusions he drew from it are unreliable because they failed to comply with four factors or criteria for drawing causal interferences from epidemiological studies: accounting for known confounders … .”), rev’d and remanded on other grounds, 618 F.3d 1127 (10th Cir. 2010), cert. denied, ___ U.S. ___ (May 24, 2012). For another example of a trial court refusing to see through important qualitative differences between and among epidemiologic studies, see In re Welding Fume Prods. Liab. Litig., 2006 WL 4507859, *33 (N.D. Ohio 2006) (reducing all studies to one level, and treating all criticisms as though they rendered all studies invalid).

6 Id.   

7 Id.

8 RMSE3d at 561-62 (“[ecological] studies may be useful for identifying associations, but they rarely provide definitive causal answers”) (internal citations omitted); see also David A. Freedman, “Ecological Inference and the Ecological Fallacy,” in Neil J. Smelser & Paul B. Baltes, eds., 6 Internat’l Encyclopedia of the Social and Behavioral Sciences 4027 (2001).

9 See also McDaniel v. CSX Transportation, Inc., 955 S.W.2d 257 (Tenn. 1997) (considering confounding but holding that it was a jury issue); Perkins v. Origin Medsystems Inc., 299 F. Supp. 2d 45 (D. Conn. 2004) (striking reliance upon a study with uncontrolled confounding, but allowing expert witness to testify anyway)

10 In re Abilifiy (Aripiprazole) Prods. Liab. Litig., 299 F. Supp. 3d 1291 (N.D. Fla. 2018).

11 Id. at 1322-23 (citing Bazemore as a purported justification for the court’s nihilistic approach); see Bazemore v. Friday, 478 U.S. 385, 400 (1986) (“Normally, failure to include variables will affect the analysis’ probativeness, not its admissibility.).

12 Id. at 1325.

Appendix – Some Federal Court Decisions on Confounding

1st Circuit

Bricklayers & Trowel Trades Internat’l Pension Fund v. Credit Suisse Sec. (USA) LLC, 752 F.3d 82, 85 (1st Cir. 2014) (affirming exclusion of expert witness whose event study and causal conclusion failed to consider relevant confounding variables and information that entered market on the event date)

2d Circuit

In re “Agent Orange” Prod. Liab. Litig., 597 F. Supp. 740, 783 (E.D.N.Y. 1984) (noting that confounding had not been sufficiently addressed in a study of U.S. servicemen exposed to Agent Orange), aff’d, 818 F.2d 145 (2d Cir. 1987) (approving district court’s analysis), cert. denied sub nom. Pinkney v. Dow Chemical Co., 484 U.S. 1004 (1988)

3d Circuit

In re Zoloft Prods. Liab. Litig., 858 F.3d 787, 793, 799 (2017) (acknowledging that statistically significant findings occur in the presence of inadequately controlled confounding or bias; affirming the exclusion of statistical expert witness, Nicholas Jewell, in part for using an admittedly non-rigorous approach to adjusting for confouding by indication)

4th Circuit

Gross v. King David Bistro, Inc., 83 F. Supp. 2d 597 (D. Md. 2000) (excluding expert witness who opined shigella infection caused fibromyalgia, given the existence of many confounding factors that muddled the putative association)

5th Circuit

Kelley v. American Heyer-Schulte Corp., 957 F. Supp. 873 (W.D. Tex. 1997) (noting that observed association may be causal or spurious, and that confounding factors must be considered to distinguish spurious from real associations)

Brock v. Merrell Dow Pharms., Inc., 874 F.2d 307, 311 (5th Cir. 1989) (noting that “[o]ne difficulty with epidemiologic studies is that often several factors can cause the same disease.”)

6th Circuit

Nelson v. Tennessee Gas Pipeline Co., WL 1297690, at *4 (W.D. Tenn. Aug. 31, 1998) (excluding an expert witness who failed to take into consideration confounding factors), aff’d, 243 F.3d 244, 252 (6th Cir. 2001), cert. denied, 534 U.S. 822 (2001)

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

7th Circuit

People Who Care v. Rockford Bd. of Educ., 111 F.3d 528, 537-38 (7th Cir. 1997) (Posner, J.) (“a statistical study that fails to correct for salient explanatory variables, or even to make the most elementary comparisons, has no value as causal explanation and is therefore inadmissible in a federal court”) (educational achievement in multiple regression);

Sheehan v. Daily Racing Form, Inc., 104 F.3d 940 (7th Cir. 1997) (holding that expert witness’s opinion, which failed to correct for any potential explanatory variables other than age, was inadmissible)

Allgood v. General Motors Corp., 2006 WL 2669337, at *11 (S.D. Ind. 2006) (noting that confounding factors must be carefully addressed; holding that selection bias rendered expert testimony inadmissible)

9th Circuit

In re Bextra & Celebrex Marketing Celebrex Sales Practices & Prod. Liab. Litig., 524 F.Supp. 2d 1166, 1178-79 (N.D. Cal. 2007) (noting plaintiffs’ expert witnesses’ inconsistent criticism of studies for failing to control for confounders; excluding opinions that Celebrex at 200 mg/day can cause heart attacks, as failing to satisfy Rule 702)

Avila v. Willits Envt’l Remediation Trust, 2009 WL 1813125, 2009 U.S. Dist. LEXIS 67981 (N.D. Cal. 2009) (excluding expert witness’s opinion in part because of his failure to rule out confounding exposures and risk factors for the outcomes of interest), aff’d in relevant part, 633 F.3d 828 (9th Cir.), cert denied, 132 S.Ct. 120 (2011)

Hendricksen v. ConocoPhillips Co., 605 F. Supp. 2d 1142, 1158 (E.D. Wash. 2009) (“In general, epidemiology studies are probative of general causation: a relative risk greater than 1.0 means the product has the capacity to cause the disease. “Where the study properly accounts for potential confounding factors and concludes that exposure to the agent is what increases the probability of contracting the disease, the study has demonstrated general causation – that exposure to the agent is capable of causing [the illness at issue] in the general population.’’) (internal quotation marks and citation omitted)

Valentine v. Pioneer Chlor Alkali Co., Inc., 921 F. Supp. 666, 677 (D. Nev. 1996) (‘‘In summary, Dr. Kilburn’s study suffers from very serious flaws. He took no steps to eliminate selection bias in the study group, he failed to identify the background rate for the observed disorders in the Henderson community, he failed to control for potential recall bias, he simply ignored the lack of reliable dosage data, he chose a tiny sample size, and he did not attempt to eliminate so-called confounding factors which might have been responsible for the incidence of neurological disorders in the subject group.’’)

Claar v. Burlington No. RR, 29 F.3d 499 (9th Cir. 1994) (affirming exclusion of plaintiffs’ expert witnesses, and grant of summary judgment, when plaintiffs’ witnesses concluded that the plaintiffs’ injuries were caused by exposure to toxic chemicals, without investigating any other possible causes).

10th Circuit

Hollander v. Sandoz Pharms. Corp., 289 F.3d 1193, 1213 (10th Cir. 2002) (affirming exclusion in Parlodel case involving stroke; confounding makes case reports inappropriate bases for causal inferences, and even observational epidemiologic studies must evaluated carefully for confounding)

D.C. Circuit

American Farm Bureau Fed’n v. EPA, 559 F.3d 512 (2009) (noting that in setting particulate matter standards addressing visibility, agency should avoid relying upon data that failed to control for the confounding effects of humidity)

Confounding in the Courts

November 2nd, 2018

Confounding in the Lower Courts

To some extent, lower courts, especially in the federal court system, got the message: Rule 702 required them to think about the evidence, and to consider threats to validity. Institutionally, there were signs of resistance to the process. Most judges were clearly much more comfortable with proxies of validity, such as qualification, publication, peer review, and general acceptance. Unfortunately for them, the Supreme Court had spoken, and then, in 2000, the Rules Committee and Congress spoke by revising Rule 702 to require a searching review of the studies upon which challenged expert witnesses were relying. Some of the cases involving confounding of one sort or another follow.

Confounding and Statistical Significance

Some courts and counsel confuse statistical significance with confounding, and suggest that a showing of statistical significance eliminates concern over confounding. This is, as several commentators have indicated, quite wrong.1 Despite the widespread criticism of this mistake in the Brock opinion, lawyers continue to repeat the mistake. One big-firm defense lawyer, for instance, claimed that “a statistically significant confidence interval helps ensure that the findings of a particular study are not due to chance or some other confounding factors.”2

Confounding and “Effect Size”

There is a role of study “effect size” in evaluating potential invalidity due to confounding, but it is frequently more nuanced than acknowledged by courts. The phrase “effect size,” of course, is misleading in that it is used to refer to the magnitude of an association, which may or may not be causal. This is one among many instances of sloppy terminology in statistical and epidemiologic science. Nonetheless, the magnitude of the relative risk may play a role in evaluating observational analytical epidemiologic studies for their ability to support a causal inference.

Small Effect Size

If the so-called effect size is low, say about 2.0, or less, actual, potential, or residual confounding (or bias) may well account for the entirety of the association.3 Many other well-known authors have concurred, with some setting the bar considerably higher, asking for risk ratios in excess of three or more, before accepting that a “clear-cut” association has been shown, unthreatened by confounding.4

Large Effect Size

Some courts have acknowledged that a strong association, with a high relative risk (without committing to what is “high”), increases the likelihood of a causal relationship, even while proceeding to ignore the effects of confounding.5 The Reference Manual suggests that a large 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.”6

*  *  *  *  *  *

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.”7

The point about “difficult to imagine” is fair enough in the context of smoking and lung cancer, but that is because no other putative confounder presents such a high relative risk in most studies. In studying other epidemiologic associations, of a high magnitude, the absence of competing risk or correlation from lurking variables would need to be independently shown, rather than relying upon the “case study” of smoking and lung cancer.

Regression and Other Statistical Analyses

The failure to include a lurking or confounding variable may render a regression analysis invalid and meaningless. The Supreme Court, however, in Bazemore, a case decided before its own decision in Daubert, and before Rule 702 was statutorily modified,8 issued a Supreme ipse dixit, to hold that the selection or omission of variables in multiple regression raises an issue that affects the weight of the analysis:

Normally, failure to include variables will affect the analysis’ probativeness, not its admissibility.”9

The Supreme Court did, however, acknowledge in Bazemore that:

There may, of course, be some regressions so incomplete as to be inadmissible as irrelevant; but such was clearly not the case here.”10

The footnote in Bazemore is telling; the majority could imagine or hypothesize a multiple regression so incomplete that it would be irrelevant, but it never thought to ask whether a relevant regression could be so incomplete as to be unreliable or invalid. The invalidity of the regression in Bazemore does not appear to have been raised as an evidentiary issue under Rule 702. None of the briefs in the Supreme Court or the judicial opinions cited or discussed Rule 702.

Despite the inappropriateness of considering the Bazemore precedent after the Court decided Daubert, many lower court decisions have treated Bazemore as dispositive of reliability challenges to regression analyses, without any meaningful discussion.11 In the last several years, however, the appellate courts have awakened on occasion to their responsibilities to ensure that opinions of statistical expert witnesses, based upon regression analyses, are evaluated through the lens of Rule 702.12

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

2 Zach Hughes, “The Legal Significance of Statistical Significance,” 28 Westlaw Journal: Pharmaceutical 1, 2 (Mar. 2012).

See 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”); 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.”).

See, e.g., 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.”); 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.”); 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.”); Ernst L. Wynder, “Epidemiological issues in weak associations,” 19 Internat’l J. Epidemiol. S5 (1990); Straus S, Richardson W, Glasziou P, Haynes R., Evidence-Based Medicine. How to Teach and Practice EBM (3d ed. 2005); Samuel Shapiro, “Bias in the evaluation of low-magnitude associations: an empirical perspective,” 151 Am. J. Epidemiol. 939 (2000); 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); 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); 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); David Sackett, R. Haynes, Gordon Guyatt, and Peter Tugwell, Clinical Epidemiology: A Basic Science for Clinical Medicine (2d ed. 1991); Gary Taubes, “Epidemiology Faces Its Limits,” 269 Science164, 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.”); 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 Instit. 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.”). See also General Causation and Epidemiologic Measures of Risk Size” (Nov. 24, 2012). Even expert witnesses for the litigation industry have agreed that small risk ratios (under two) are questionable for potential and residual confounding. 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.”)

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”).

RMSE3d at 219.

RMSE3d at 602. See 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.”).

See Federal Rule of Evidence 702, Pub. L. 93–595, § 1, Jan. 2, 1975, 88 Stat. 1937; Apr. 17, 2000 (eff. Dec. 1, 2000); Apr. 26, 2011, eff. Dec. 1, 2011.)

Bazemore v. Friday, 478 U.S. 385, 400 (1986) (reversing Court of Appeal’s decision that would have disallowed a multiple regression analysis that omitted important variables).

10 Id. at 400 n. 10.

11 See, e.g., Manpower, Inc. v. Insurance Company of the State of Pennsylvania, 732 F.3d 796, 799 (7th Cir., 2013) (“the Supreme Court and this Circuit have confirmed on a number of occasions that the selection of the variables to include in a regression analysis is normally a question that goes to the probative weight of the analysis rather than to its admissibility.”); Cullen v. Indiana Univ. Bd. of Trustees, 338 F.3d 693, 701‐02 & n.4 (7th Cir. 2003) (citing Bazemore in rejecting challenge to expert witness’s omission of variables in regression analysis); In re High Fructose Corn Syrup Antitrust Litigation, 295 F.3d 651, 660‐61 (7th Cir. 2002) (refusing to exclude expert witness opinion testimony based upon regression analyses, flawed by omission of key variables); Adams v. Ameritech Servs., Inc., 231 F.3d 414, 423 (7th Cir. 2000) (relying upon Bazemore to affirm statistical analysis based upon correlation with no regression analysis). See also The Seventh Circuit Regresses on Rule 702” (Oct. 29, 2013).

12 See, e.g., ATA Airlines, Inc. v. Fed. Express Corp., 665 F.3d 882, 888–89 (2011) (Posner, J.) (reversing on grounds that plaintiff’s regression analysis should never have been admitted), cert. denied, 2012 WL 189940 (Oct. 7, 2012); Zenith Elec. Corp. v. WH-TV Broad. Corp., 395 F.3d 416 (7th Cir.) (affirming exclusion of expert witness opinion whose extrapolations were mere “ipse dixit”), cert. denied, 125 S. Ct. 2978 (2005); Sheehan v. Daily Racing Form, Inc. 104 F.3d 940 (7th Cir. 1997) (Posner, J.) (discussing specification error). See also Munoz v. Orr, 200 F.3d 291 (5th Cir. 2000). For a more enlightened and educated view of regression and the scope and application of Rule 702, from another Seventh Circuit panel, Judge Posner’s decision in ATA Airlines, supra, is a good starting place. SeeJudge Posner’s Digression on Regression” (April 6, 2012).

Rule 702 Requires Courts to Sort Out Confounding

October 31st, 2018


Back in 2000, several law professors wrote an essay, in which they detailed some of the problems courts experienced in expert witness gatekeeping. Their article noted that judges easily grasped the problem of generalizing from animal evidence to human experience, and thus they simplistically emphasized human (epidemiologic) data. But in their emphasis on the problems in toxicological evidence, the judges missed problems of internal validity, such as confounding, in epidemiologic studies:

Why do courts have such a preference for human epidemiological studies over animal experiments? Probably because the problem of external validity (generalizability) is one of the most obvious aspects of research methodology, and therefore one that non-scientists (including judges) are able to discern with ease – and then give excessive weight to (because whether something generalizes or not is an empirical question; sometimes things do and other times they do not). But even very serious problems of internal validity are harder for the untrained to see and understand, so judges are slower to exclude inevitably confounded epidemiological studies (and give insufficient weight to that problem). Sophisticated students of empirical research see the varied weaknesses, want to see the varied data, and draw more nuanced conclusions.”2

I am not sure that the problems are dependent in the fashion suggested by the authors, but their assessment that judges may be reluctant to break the seal on the black box of epidemiology, and that judges frequently lack the ability to make nuanced evaluations of the studies on which expert witnesses rely seems fair enough. Judges continue to miss important validity issues, perhaps because the adversarial process levels all studies to debating points in litigation.3

The frequent existence of validity issues undermines the partisan suggestion that Rule 702 exclusions are merely about “sufficiency of the evidence.” Sometimes, there is just too much of nothing to rise even to a problem of insufficiency. Some studies are “not even wrong.”4 Similarly, validity issues are an embarrassment to those authors who argue that we must assemble all the evidence and consider the entirety under ethereal standards, such as “weight of the evidence,” or “inference to the best explanation.” Sometimes, some or much of the available evidence does not warrant inclusion in the data set at all, and any causal inference is unacceptable.

Threats to validity come in many forms, but confounding is a particularly dangerous one. In claims that substances such as diesel fume or crystalline silica cause lung cancer, confounding is a huge problem. The proponents of the claims suggest relative risks in the range of 1.1 to 1.6 for such substances, but tobacco smoking results in relative risks in excess of 20, and some claim that passive smoking at home or in the workplace results in relative risks of the same magnitude as the risk ratios claimed for diesel particulate or silica. Furthermore the studies behind these claims frequently involve exposures to other known or suspected lung carcinogens, such as arsenic, radon, dietary factors, asbestos, and others.

Definition of Confounding

Confounding results from the presence of a so-called confounding (or lurking) variable, helpfully defined in the chapter on statistics in the Reference Manual on Scientific Evidence:

confounding variable; confounder. A confounder is correlated with the independent variable and the dependent variable. An association between the dependent and independent variables in an observational study may not be causal, but may instead be due to confounding. See controlled experiment; observational study.”5

This definition suggests that the confounder need not be known to cause the dependent variable/outcome; the confounder need be only correlated with the outcome and an independent variable, such as exposure. Furthermore, the confounder may be actually involved in such a way as to increase or decrease the estimated relationship between dependent and independent variables. A confounder that is known to be present typically is referred to as a an “actual” confounder, as opposed to one that may be at work, and known as a “potential” confounder. Furthermore, even after exhausting known and potential confounders, studies of may be affected by “residual” confounding, especially when the total array of causes of the outcome of interest is not understood, and these unknown causes are not randomly distributed between exposed and unexposed groups in epidemiologic studies. Litigation frequently involves diseases or outcomes with unknown causes, and so the reality of unidentified residual confounders is unavoidable.

In some instances, especially in studies pharmaceutical adverse outcomes, there is the danger that the hypothesized outcome is also a feature of the underlying disease being treated. This phenomenon is known as confounding by indication, or as indication bias.6

Kaye and Freedman’s statistics chapter notes that confounding is a particularly important consideration when evaluating observational studies. In randomized clinical trials, one goal of the randomization is the elimination of the role of bias and confounding by the random assignment of exposures:

2. Randomized controlled experiments

In randomized controlled experiments, investigators assign subjects to treatment or control groups at random. The groups are therefore likely to be comparable, except for the treatment. This minimizes the role of confounding.”7

In observational studies, confounding may completely invalidate an association. Kaye and Freedman give an example from the epidemiologic literature:

Confounding remains a problem to reckon with, even for the best observational research. For example, women with herpes are more likely to develop cervical cancer than other women. Some investigators concluded that herpes caused cancer: In other words, they thought the association was causal. Later research showed that the primary cause of cervical cancer was human papilloma virus (HPV). Herpes was a marker of sexual activity. Women who had multiple sexual partners were more likely to be exposed not only to herpes but also to HPV. The association between herpes and cervical cancer was due to other variables.”8

The problem identified as confounding by Freedman and Kaye cannot be dismissed as an issue that goes to the “weight” of the study issue; the confounding goes to the heart of the ability of the herpes studies to show an association that can be interpreted to be causal. Invalidity from confounding renders the studies “weightless” in any “weight of the evidence” approach. There are, of course, many ways to address confounding in studies: stratification, multivariate analyses, multiple regression, propensity scores, etc. Consideration of the propriety and efficacy of these methods is a whole other level of analysis, which does not arise unless and until the threshold question of confounding is addressed.

Reference Manual on Scientific Evidence

The epidemiology chapter of the Second Edition of the Manual stated that ruling out of confounding as an obligation of the expert witness who chooses to rely upon the study.9 Although the same chapter in the Third Edition occasionally waffles, its authors come down on the side of describing confounding as a threat to validity, which must be ruled out before the study can be relied upon. In one place, the authors indicate “care” is required, and that analysis for random error, confounding, bias “should be conducted”:

Although relative risk is a straightforward concept, care must be taken in interpreting it. Whenever an association is uncovered, further analysis should be conducted to assess whether the association is real or a result of sampling error, confounding, or bias. These same sources of error may mask a true association, resulting in a study that erroneously finds no association.”10

Elsewhere in the same chapter, the authors note that “chance, bias, and confounding” must be looked at, but again, the authors stop short of noting that these threats to validity must be eliminated:

Three general categories of phenomena can result in an association found in a study to be erroneous: chance, bias, and confounding. Before any inferences about causation are drawn from a study, the possibility of these phenomena must be examined.”11

                *  *  *  *  *  *  *  *

To make a judgment about causation, a knowledgeable expert must consider the possibility of confounding factors.”12

Eventually, however, the epidemiology chapter takes a stand, and an important one:

When researchers find an association between an agent and a disease, it is critical to determine whether the association is causal or the result of confounding.”13

Mandatory Not Precatory

The better reasoned cases decided under Federal Rule of Evidence 702, and state-court analogues, follow the Reference Manual in making clear that confounding factors must be carefully addressed and eliminated. Failure to rule out the role of confounding renders a conclusion of causation, reached in reliance upon confounded studies, invalid.14

The inescapable mandate of Rules 702 and 703 is to require judges to evaluate the bases of a challenged expert witness’s opinion. Threats to internal validity, such as confounding, in a study may make reliance upon any given study, or an entire set of studies, unreasonable, which thus implicates Rule 703. Importantly, stacking up more invalid studies does not overcome the problem by presenting a heap of evidence, incompetent to show anything.


Before the Supreme Court decided Daubert, few federal or state courts were willing to roll up their sleeves to evaluate the internal validity of relied upon epidemiologic studies. Issues of bias and confounding were typically dismissed by courts as issues that went to “weight, not admissibility.”

Judge Weinstein’s handling of the Agent Orange litigation, in the mid-1980s, marked a milestone in judicial sophistication and willingness to think critically about the evidence that was being funneled into the courtroom.15 The Bendectin litigation also was an important proving ground in which the defendant pushed courts to keep their eyes and minds open to issues of random error, bias, and confounding, when evaluating scientific evidence, on both pre-trial and on post-trial motions.16


When the United States Supreme Court addressed the admissibility of plaintiffs’ expert witnesses in Daubert, its principal focus was on the continuing applicability of the so-called Frye rule after the enactment of the Federal Rules of Evidence. The Court left the details of applying the then newly clarified “Daubert” standard to the facts of the case on remand to the intermediate appellate court. The Ninth Circuit, upon reconsidering the case, re-affirmed the trial court’s previous grant of summary judgment, on grounds of the plaintiffs’ failure to show specific causation.

A few years later, the Supreme Court itself engaged with the actual evidentiary record on appeal, in a lung cancer claim, which had been dismissed by the district court. Confounding was one among several validity issues in the studies relied upon by plaintiffs” expert witnesses. The Court concluded that the plaintiffs’ expert witnesses’ bases did not individually or collectively support their conclusions of causation in a reliable way. With respect to one particular epidemiologic study, the Supreme Court observed that a study that looked at workers who “had been exposed to numerous potential carcinogens” could not show that PCBs cause lung cancer. General Elec. Co. v. Joiner, 522 U.S. 136, 146 (1997).17

1 An earlier version of this post can be found at “Sorting Out Confounded Research – Required by Rule 702” (June 10, 2012).

2 David Faigman, David Kaye, Michael Saks, and Joseph Sanders, “How Good is Good Enough? Expert Evidence Under Daubert andKumho,” 50Case Western Reserve L. Rev. 645, 661 n.55 (2000).

3 See, e.g., In re Welding Fume Prods. Liab. Litig., 2006 WL 4507859, *33 (N.D.Ohio 2006) (reducing all studies to one level, and treating all criticisms as though they rendered all studies invalid).

4 R. Peierls, “Wolfgang Ernst Pauli, 1900-1958,” 5Biographical Memoirs of Fellows of the Royal Society 186 (1960) (quoting Wolfgang Pauli’s famous dismissal of a particularly bad physics paper).

5 David Kaye & David Freedman, “Reference Guide on Statistics,” inReference Manual on Scientific Evidence 211, 285 (3d ed. 2011)[hereafter theRMSE3d].

6 See, e.g., R. Didham, et al., “Suicide and Self-Harm Following Prescription of SSRIs and Other Antidepressants: Confounding By Indication,” 60Br. J. Clinical Pharmacol. 519 (2005).

7 RMSE3d at 220.

8 RMSE3d at 219 (internal citations omitted).

9 Reference Guide on Epidemiology at 369 -70 (2ed 2000) (“Even if an association is present, epidemiologists must still determine whether the exposure causes the disease or if a confounding factor is wholly or partly responsible for the development of the outcome.”).

10 RMSE3d at 567-68 (internal citations omitted).

11 RMSE3d at 572.

12 RMSE3d at 591 (internal citations omitted).

13 RMSE3d at 591

14 Similarly, an exonerative conclusion of no association might be vitiated by confounding with a protective factor, not accounted for in a multivariate analysis. Practically, such confounding seems less prevalent than confounding that generates a positive association.

15 In re “Agent Orange” Prod. Liab. Litig., 597 F. Supp. 740, 783 (E.D.N.Y. 1984) (noting that confounding had not been sufficiently addressed in a study of U.S. servicemen exposed to Agent Orange), aff’d, 818 F.2d 145 (2d Cir. 1987) (approving district court’s analysis), cert. denied sub nom. Pinkney v. Dow Chemical Co., 484 U.S. 1004 (1988).

16 Brock v. Merrell Dow Pharms., Inc., 874 F.2d 307, 311 , modified on reh’g, 884 F.2d 166 (5th Cir. 1989) (noting that “[o]ne difficulty with epidemiologic studies is that often several factors can cause the same disease.”)

17 The Court’s discussion related to the reliance of plaintiffs’ expert witnesses upon, among other studies, Kuratsune, Nakamura, Ikeda, & Hirohata, “Analysis of Deaths Seen Among Patients with Yusho – A Preliminary Report,” 16 Chemosphere 2085 (1987).

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