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Reference Manual on Scientific Evidence v4.0

February 28th, 2021

The need for revisions to the third edition of the Reference Manual on Scientific Evidence (RMSE) has been apparent since its publication in 2011. A decade has passed, and the federal agencies involved in the third edition, the Federal Judicial Center (FJC) and the National Academies of Science Engineering and Medicine (NASEM), are assembling staff to prepare the long-needed revisions.

The first sign of life for this new edition came back on November 24, 2020, when the NASEM held a short, closed door virtual meeting to discuss planning for a fourth edition.[1] The meeting was billed by the NASEM as “the first meeting of the Committee on Emerging Areas of Science, Engineering, and Medicine for the Courts: Identifying Chapters for a Fourth Edition of The Reference Manual on Scientific Evidence.” The Committee members heard from John S. Cooke (FJC Director), and Alan Tomkins and Reggie Sheehan, both of the National Science Foundation (NSF). The stated purpose of the meeting was to review the third edition of the RMSE to identify “identify areas of science, technology, and medicine that may be candidates for new or updated chapters in a proposed new (fourth) edition of the manual.” The only public pronouncement from the first meeting was that the committee would sponsor a workshop on the topic of new chapters for the RMSE, in early 2021.

The Committee’s second meeting took place a week later, again in closed session.[2] The stated purpose of the Committee’s second meeting was to review the third edition of the RMSE, and to discuss candidate areas for inclusion as new and updated chapters for a fourth edition.

Last week saw the Committee’s third, public meeting. The meeting spanned two days (Feb. 24 and 25, 2021), and was open to the public. The meeting was sponsored by NASEM, FJC, along with the NSF, and was co-chaired by Thomas D. Albright, Professor and Conrad T. Prebys Chair at the Salk Institute for Biological Studies, and the Hon. Kathleen McDonald O’Malley, who sits on the United States Court of Appeals for the Federal Circuit. Identified members of the committee include:

Steven M. Bellovin, professor in the Computer Science department at Columbia University;

Karen Kafadar, Departmental Chair and Commonwealth Professor of Statistics at the University of Virginia, and former president of the American Statistical Association;

Andrew Maynard, professor, and director of the Risk Innovation Lab at the School for the Future of Innovation in Society, at Arizona State University;

Venkatachalam Ramaswamy, Director of the Geophysical Fluid Dynamics Laboratory of the National Oceanic and Atmospheric Administration (NOAA) Office of Oceanic and Atmospheric Research (OAR), studying climate modeling and climate change;

Thomas Schroeder, Chief Judge for the U.S. District Court for the Middle District of North Carolina;

David S. Tatel, United States Court of Appeals for the District of Columbia Circuit; and

Steven R. Kendall, Staff Officer

The meeting comprised five panel presentations, made up of remarkably accomplished and talented speakers. Each panel’s presentations were followed by discussion among the panelists, and the committee members. Some panels answered questions submitted from the public audience. Judge O’Malley opened the meeting with introductory remarks about the purpose and scope of the RMSE, and of the inquiry into additional possible chapters.

  1. Challenges in Evaluating Scientific Evidence in Court

The first panel consisted entirely of judges, who held forth on their approaches to judicial gatekeeping of expert witnesses, and their approach to scientific and technical issues. Chief Judge Schroeder moderated the presentations of panelists:

Barbara Parker Hervey, Texas Court of Criminal Appeals;

Patti B. Saris, Chief Judge of the United States District Court for the District of Massachusetts,  member of President’s Council of Advisors on Science and Technology (PCAST);

Leonard P. Stark, U.S. District Court for the District of Delaware; and

Sarah S. Vance, Judge (former Chief Judge) of the U.S. District Court for the Eastern District of Louisiana, chair of the Judicial Panel on Multidistrict Litigation.

  1. Emerging Issues in the Climate and Environmental Sciences

Paul Hanle, of the Environmental Law Institute moderated presenters:

Joellen L. Russell, the Thomas R. Brown Distinguished Chair of Integrative Science and Professor at the University of Arizona in the Department of Geosciences;

Veerabhadran Ramanathan, Edward A. Frieman Endowed Presidential Chair in Climate Sustainability at the Scripps Institution of Oceanography at the University of California, San Diego;

Benjamin D. Santer, atmospheric scientist at Lawrence Livermore National Laboratory; and

Donald J. Wuebbles, the Harry E. Preble Professor of Atmospheric Science at the University of Illinois.

  1. Emerging Issues in Computer Science and Information Technology

Josh Goldfoot, Principal Deputy Chief, Computer Crime & Intellectual Property Section, at U.S. Department of Justice, moderated panelists:

Jeremy J. Epstein, Deputy Division Director of Computer and Information Science and Engineering (CISE) and Computer and Network Systems (CNS) at the National Science Foundation;

Russ Housley, founder of Vigil Security, LLC;

Subbarao Kambhampati, professor of computer science at Arizona State University; and

Alice Xiang, Senior Research Scientist at Sony AI.

  1. Emerging Issues in the Biological Sciences

Panel four was moderated by Professor Ellen Wright Clayton, the Craig-Weaver Professor of Pediatrics, and Professor of Law and of Health Policy at Vanderbilt Law School, at Vanderbilt University. Her panelists were:

Dana Carroll, distinguished professor in the Department of Biochemistry at the University of Utah School of Medicine;

Yaniv Erlich, Chief Executive Officer of Eleven Therapeutics, Chief Science Officer of MyHeritage;

Steven E. Hyman, director of the Stanley Center for Psychiatric Research at Broad Institute of MIT and Harvard; and

Philip Sabes, Professor Emeritus in Physiology at the University of California, San Francisco (UCSF).

  1. Emerging areas in Psychology, Data, and Statistical Sciences

Gary Marchant, Lincoln Professor of Emerging Technologies, Law and Ethics, at Arizona State University’s Sandra Day O’Connor College of Law, moderated panelists:

Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, Harvard University, and the Founding Editor-in-Chief of Harvard Data Science Review;

Rebecca Doerge, Glen de Vries Dean of the Mellon College of Science at Carnegie Mellon University, member of the Dietrich College of Humanities and Social Sciences’ Department of Statistics and Data Science, and of the Mellon College of Science’s Department of Biological Sciences;

Daniel Kahneman, Professor of Psychology and Public Affairs Emeritus at the Princeton School of Public and International Affairs, the Eugene Higgins Professor of Psychology Emeritus at Princeton University, and a fellow of the Center for Rationality at the Hebrew University in Jerusalem; and

Goodwin Liu, Associate Justice of the California Supreme Court.

The Proceedings of this two day meeting were recorded and will be published. The website materials are unclear whether the verbatim remarks will be included, but regardless, the proceedings should warrant careful reading.

Judge O’Malley, in her introductory remarks, emphasized that the RMSE must be a neutral, disinterested source of information for federal judges, an aspirational judgment from which there can be no dissent. More controversial will be Her Honor’s assessment that epidemiologic studies can “take forever,” and other judges’ suggestion that plaintiffs lack financial resources to put forward credible, reliable expert witnesses. Judge Vance corrected the course of the discussion by pointing out that MDL plaintiffs were not disadvantaged, but no one pointed out that plaintiffs’ counsel were among the wealthiest individuals in the United States, and that they have been known to sponsor epidemiologic and other studies that wind up as evidence in court.

Panel One was perhaps the most discomforting experience, as it involved revelations about how sausage is made in the gatekeeping process. The panel was remarkable for including a state court judge from Texas, Judge Barbara Parker Hervey, of the Texas Court of Criminal Appeals. Judge Hervey remarked that [in her experience] if we judges “can’t understand it, we won’t read it.” Her dictum raises interesting issues. No doubt, in some instances, the judicial failure of comprehension is the fault of the lawyers. What happens when the judges “can’t understand it”? Do they ask for further briefing? Or do they ask for a hearing with viva voce testimony from expert witnesses? The point was not followed up.

Leonard P. Stark’s insights were interesting in that his docket in the District of Delaware is flooded with patent and Hatch-Waxman Act litigation. Judge Stark’s extensive educational training is in politics and political science. The docket volume Judge Stark described, however, raised issues about how much attention he could give to any one case.

When the panel was asked how they dealt with scientific issues, Judge Saris discussed her presiding over In re Neurontin, which was a “big challenge for me to understand,” with no randomized trials or objective assessments by the litigants.[3] Judge Vance discussed her experience of presiding in a low-level benzene exposure case, in which plaintiff claimed that his acute myelogenous leukemia was caused by gasoline.[4]

Perhaps the key difference in approach to Rule 702 emerged when the judges were asked whether they read the underlying studies. Judge Saris did not answer directly, but stated she reads the reports. Judge Vance, on the other hand, noted that she reads the relied upon studies. In her gasoline-leukemia case, she read the relied-upon epidemiologic studies, which she described as a “hodge podge,” and which were misrepresented by the expert witnesses and counsel. She emphasized the distortions of the adversarial system and the need to moderate its excesses by validating what exactly the expert witnesses had relied upon.

This division in judicial approach was seen again when Professor Karen Kafadar asked how the judges dealt with peer review. Judge Saris seemed to suggest that the peer-reviewed published article was prima facie reliable. Others disagreed and noted that peer reviewed articles can have findings that are overstated, and wrong. One speaker noted that Jerome Kassirer had downplayed the significance of, and the validation provided by, peer review, in the RMSE (3rd ed 2011).

Curiously, there was no discussion of Rule 703, either in Judge O’Malley’s opening remarks on the RMSE, or in the first panel discussion. When someone from the audience submitted a question about the role of Rule 703 in the gatekeeping process, the moderator did not read it.

Panel Two. The climate change panel was a tour de force of the case for anthropogenic climate change. To some, the presentations may have seemed like a reprise of The Day After Tomorrow. Indeed, the science was presented so confidently, if not stridently, that one of the committee members asked whether there could be any reasonable disagreement. The panelists responded essentially by pointing out that there could be no good faith opposition. The panelists were much less convincing on the issue of attributability. None of the speakers addressed the appropriateness vel non of climate change litigation, when the federal and state governments encouraged, licensed, and regulated the exploitation and use of fossil fuel reserves.

Panel Four. Dr. Clayton’s panel was fascinating and likely to lead to new chapters. Professor Hyman presented on heritability, a subject that did not receive much attention in the RMSE third edition. With the advent of genetic claims of susceptibility and defenses of mutation-induced disease, courts will likely need some good advice on navigating the science. Dana Carroll presented on human genome editing (CRISPR). Philip Sabes presented on brain-computer interfaces, which have progressed well beyond the level of sci-fi thrillers, such as The Brain That Wouldn’t Die (“Jan in the Pan”).

In addition to the therapeutic applications, Sabes discussed some of potential forensic uses, such as lie detectors, pain quantification, and the like. Yaniv Erlich, of MyHeritage, discussed advances in forensic genetic genealogy, which have made a dramatic entrance to the common imagination through the apprehension of Joseph James DeAngelo, the Golden State killer. The technique of triangulating DNA matches from consumer DNA databases has other applications, of course, such as identifying lost heirs, and resolving paternity issues.

Panel Five. Professor Marchant’s panel may well have identified some of the most salient needs for the next edition of the RMSE. Nobel Laureate Daniel Kahneman presented some of the highlights from his forthcoming book about “noise” in human judgment.[5] Kahneman’s expansion upon his previous thinking about the sources of error in human – and scientific – judgment are a much needed addition to the RMSE. Along the same lines, Professor Xiao Li Meng, presented on selection bias, and how it pervades scientific work, and detracts from the strength of evidence in the form of:

  1. cherry picking
  2. subgroup analyses
  3. unprincipled handling of outliers
  4. selection in methodologies (different tests)
  5. selection in due diligence (check only when you don’t like results)
  6. publication bias that results from publishing only impressive or statistically significant results
  7. selection in reporting, not reporting limitations all analyses
  8. selection in understanding

Professor Meng’s insights are sorely lacking in the third edition of the RMSE, and among judicial gatekeepers generally.  All too often, undue selectivity in methodologies and in relied-upon data is treated by judges as an issue that “goes to the weight, not the admissibility” of expert witness opinion testimony. In actuality, the selection biases, and other systematic and cognitive biases, are as important as, if not more important than, random error assessments. Indeed a close look at the RMSE third edition reveals a close embrace of the amorphous, anything-goes “weight of the evidence” approach in the epidemiology chapter.  That chapter marginalizes meta-analyses and fails to mention systematic review techiniques altogether. The chapter on clinical medicine, however, takes a divergent approach, emphasizing the hierarchy of evidence inherent in different study types, and the need for principled and systematic reviews of the available evidence.[6]

The Committee co-chairs and panel moderators did a wonderful job to identify important new trends in genetics, data science, error assessment, and computer science, and they should be congratulated for their efforts. Judge O’Malley is certainly correct in saying that the RMSE must be a neutral source of information on statistical and scientific methodologies, and it needs to be revised and updated to address errors and omissions in the previous editions. The legal community should look for, and study, the published proceedings when they become available.

——————————————————————————————————

[1]  See “Emerging Areas of Science, Engineering, and Medicine for the Courts: Identifying Chapters for a Fourth Edition of The Reference Manual on Scientific Evidence – Committee Meeting” (Nov. 24, 2020).

[2]  See “Emerging Areas of Science, Engineering, and Medicine for the Courts: Identifying Chapters for a Fourth Edition of The Reference Manual on Scientific Evidence – Committee Meeting 2 (Virtual)” (Dec. 1, 2020).

[3]  In re Neurontin Marketing, Sales Practices & Prods. Liab. Litig., 612 F. Supp. 2d 116 (D. Mass. 2009) (Saris, J.).

[4]  Burst v. Shell Oil Co., 104 F.Supp.3d 773 (E.D.La. 2015) (Vance, J.), aff’d, ___ Fed. App’x ___, 2016 WL 2989261 (5th Cir. May 23, 2016), cert. denied, 137 S.Ct. 312 (2016). See “The One Percent Non-solution – Infante Fuels His Own Exclusion in Gasoline Leukemia Case” (June 25, 2015).

[5]  Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein, Noise: A Flaw in Human Judgment (anticipated May 2021).

[6]  See John B. Wong, Lawrence O. Gostin, and Oscar A. Cabrera, “Reference Guide on Medical Testimony,” Reference Manual on Scientific Evidence 723-24 (3ed ed. 2011) (discussing hierarchy of medical evidence, with systematic reviews at the apex).

Posted in Evidence, Expert Witnesses, Peer Review, Reference Manual on Scientific Evidence, Rule 702, Rule 703, Scientific Evidence | 90 Comments »

David Madigan’s Graywashed Meta-Analysis in Taxotere MDL

June 12th, 2020

Once again, a meta-analysis is advanced as a basis for an expert witness’s causation opinion, and once again, the opinion is the subject of a Rule 702 challenge. The litigation is In re Taxotere (Docetaxel) Products Liability Litigation, a multi-district litigation (MDL) proceeding before Judge Jane Triche Milazzo, who sits on the United States District Court for the Eastern District of Louisiana.

Taxotere is the brand name for docetaxel, a chemotherapic medication used either alone or in conjunction with another chemotherapy, to treat a number of different cancers. Hair loss is a side effect of Taxotere, but in the MDL, plaintiffs claim that they have experienced permanent hair loss, which was not adequately warned about in their view. The litigation thus involved issues of exactly what “permanent” means, medical causation, adequacy of warnings in the Taxotere package insert, and warnings causation.

Defendant Sanofi challenged plaintiffs’ statistical expert witness, David Madigan, a frequent testifier for the lawsuit industry. In its Rule 702 motion, Sanofi argued that Madigan had relied upon two randomized clinical trials (TAX 316 and GEICAM 9805) that evaluated “ongoing alopecia” to reach conclusions about “permanent alopecia.” Sanofi made the point that “ongoing” is not “permanent,” and that trial participants who had ongoing alopecia may have had their hair grow back. Madigan’s reliance upon an end point different from what plaintiffs complained about made his analysis irrelevant. The MDL court rejected Sanofi’s argument, with the observation that Madigan’s analysis was not irrelevant for using the wrong end point, only less persuasive, and that Sanofi’s criticism was one that “Sanofi can highlight for the jury on cross-examination.”[1]

Did Judge Milazzo engage in judicial dodging with rejecting the relevancy argument and emphasizing the truism that Sanofi could highlight the discrepancy on cross-examination?  In the sense that the disconnect can be easily shown by highlight the different event rates for the alopecia differently defined, the Sanofi argument seems like one that a jury could easily grasp and refute. The judicial shrug, however, begs the question why the defendant should have to address a data analysis that does not support the plaintiffs’ contention about “permanence.” The federal rules are supposed to advance the finding of the truth and the fair, speedy resolution of cases.

Sanofi’s more interesting argument, from the perspective of Rule 702 case law, was its claim that Madigan had relied upon a flawed methodology in analyzing the two clinical trials:

“Sanofi emphasizes that the results of each study individually produced no statistically significant results. Sanofi argues that Dr. Madigan cannot now combine the results of the studies to achieve statistical significance. The Court rejects Sanofi’s argument and finds that Sanofi’s concern goes to the weight of Dr. Madigan’s testimony, not to its admissibility.34”[2]

There seems to be a lot going on in the Rule 702 challenge that is not revealed in the cryptic language of the MDL district court. First, the court deployed the jurisprudentially horrific, conclusory language to dismiss a challenge that “goes to the weight …, not to … admissibility.” As discussed elsewhere, this judicial locution is rarely true, fails to explain the decision, and shows a lack of engagement with the actual challenge.[3] Of course, aside from the inanity of the expression, and the failure to explain or justify the denial of the Rule 702 challenge, the MDL court may have been able to provide a perfectly adequately explanation.

Second, the footnote in the quoted language, number 34, was to the infamous Milward case,[4] with the explanatory parenthetical that the First Circuit had reversed a district court for excluding testimony of an expert witness who had sought to “draw conclusions based on combination of studies, finding that alleged flaws identified by district court go to weight of testimony not admissibility.”[5] As discussed previously, the widespread use of the “weight not admissibility” locution, even by the Court of Appeals, does not justify it. More important, however, the invocation of Milward suggests that any alleged flaws in combining study results in a meta-analysis are always matters for the jury, no matter how arcane, technical, or threatening to validity they may be.

So was Judge Milazzo engaged in judicial dodging in Her Honor’s opinion in Taxotere? Although the citation to Milward tends to inculpate, the cursory description of the challenge raises questions whether the challenge itself was valid in the first place. Fortunately, in this era of electronic dockets, finding the actual Rule 702 motion is not very difficult, and we can inspect the challenge to see whether it was dodged or given short shrift. Remarkably, the reality is much more complicated than the simple, simplistic rejection by the MDL court would suggest.

Sanofi’s brief attacks three separate analyses proffered by David Madigan, and not surprisingly, the MDL court did not address every point made by Sanofi.[6] Sanofi’s point about the inappropriateness of conducting the meta-analysis was its third in its supporting brief:

“Third, Dr. Madigan conducted a statistical analysis on the TAX316 and GEICAM9805/TAX301 clinical trials separately and combined them to do a ‘meta-analysis’. But Dr. Madigan based his analysis on unproven assumptions, rendering his methodology unreliable. Even without those assumptions, Dr. Madigan did not find statistical significance for either of the clinical trials independently, making this analysis unhelpful to the trier of fact.”[7]

This introductory statement of the issue is itself not particularly helpful because it fails to explain why combining two individual clinical trials (“RCTs”), each not having “statistically significant” results, by meta-analysis would be unhelpful. Sanofi’s brief identified other problems with Madigan’s analyses, but eventually returned to the meta-analysis issue, with the heading:

“Dr. Madigan’s analysis of the individual clinical trials did not result in statistical significance, thus is unhelpful to the jury and will unfairly prejudice Sanofi.”[8]

After a discussion of some of the case law about statistical significance, Sanofi pressed its case against Madigan. Madigan’s statistical analysis of each of two RCTs apparently did not reach statistical significance, and Sanofi complained that permitting Madigan to present these two analyses with results that were “not statistically very impressive,” would confuse and mislead the jury.[9]

“Dr. Madigan tried to avoid that result here [of having two statistically non-significant results] by conducting a ‘meta-analysis’ — a greywashed term meaning that he combined two statistically insignificant results to try to achieve statistical significance. Madigan Report at 20 ¶ 53. Courts have held that meta-analyses are admissible, but only when used to reduce the numerical instability on existing statistically significant differences, not as a means to achieve statistical significance where it does not exist. RMSE at 361–362, fn76.”

Now the claims here are quite unsettling, especially considering that they were lodged in a defense brief, in an MDL, with many cases at stake, made on behalf of an important pharmaceutical company, represented by two large, capable national or international law firms.

First, what does the defense brief signify by placing ‘meta-analysis’ in quotes. Are these scare quotes to suggest that Madigan was passing off something as a meta-analysis that failed to be one? If so, there is nothing in the remainder of the brief that explains such an interpretation. Meta-analysis has been around for decades, and reporting meta-analyses of observational or of experimental studies has been the subject of numerous consensus and standard-setting papers over the last two decades. Furthermore, the FDA has now issued a draft guidance for the use of meta-analyses in pharmacoepidemiology. Scare quotes are at best unexplained, at worst, inappropriate. If the authors had something else in mind, they did not explain the meaning of using quotes around meta-analysis.

Second, the defense lawyers referred to meta-analysis as a “greywashed” term. I am always eager to expand my vocabulary, and so I looked up the word in various dictionaries of statistical and epidemiologic terms. Nothing there. Perhaps it was not a technical term, so I checked with the venerable Oxford English Dictionary. No relevant entries.

Pushed to the wall, I checked the font of all knowledge – the internet. To be sure, I found definitions, but nothing that could explain this odd locution in a brief filed in an important motion:

gray-washing: “noun In calico-bleaching, an operation following the singeing, consisting of washing in pure water in order to wet out the cloth and render it more absorbent, and also to remove some of the weavers’ dressing.”

graywashed: “adj. adopting all the world’s cultures but not really belonging to any of them; in essence, liking a little bit of everything but not everything of a little bit.”

Those definitions do not appear pertinent.

Another website offered a definition based upon the “blogsphere”:

Graywash: “A fairly new term in the blogsphere, this means an investigation that deals with an offense strongly, but not strongly enough in the eyes of the speaker.”

Hmmm. Still not on point.

Another one from “Urban Dictionary” might capture something of what was being implied:

Graywashing: “The deliberate, malicious act of making art having characters appear much older and uglier than they are in the book, television, or video game series.”

Still, I am not sure how this is an argument that a federal judge can respond to in a motion affecting many cases.

Perhaps, you say, I am quibbling with word choices, and I am not sufficiently in tune with the way people talk in the Eastern District of Louisiana. I plead guilty to both counts. But the third, and most important point, is the defense assertion that meta-analyses are only admissible “when used to reduce the numerical instability on existing statistically significant differences, not as a means to achieve statistical significance where it does not exist.”

This assertion is truly puzzling. Meta-analyses involve so many layers of hearsay that they will virtually never be admissible. Admissibility of the meta-analyses is virtually never the issue. When an expert witness has conducted a meta-analysis, or has relied upon one, the important legal question is whether the witness may reasonably rely upon the meta-analysis (under Rule 703) for an inference that satisfies Rule 702. The meta-analysis itself does not come into evidence, and does not go out to the jury for its deliberations.

But what about the defense brief’s “only when” language that clearly implies that courts have held that expert witnesses may rely upon meta-analyses only to reduce “numerical instability on existing statistically significant differences”? This seems clearly wrong because achieving statistical significance from studies that have no “instability” for their point estimates but individually lack statistical significance is a perfectly legitimate and valid goal. Consider a situation in which, for some reason, sample size in each study is limited by the available observations, but we have 10 studies, each with a point estimate of 1.5, and each with a 95% confidence interval of (0.88, 2.5). This hypothetical situation presents no instability of point estimates, and the meta-analytical summary point estimate would shrink the confidence interval so that the lower bound would exclude 1.0, in a perfectly valid analysis. In the real world, meta-analyses are conducted on studies with point estimates of risk that vary, because of random and non-random error, but there is no reason that meta-analyses cannot reduce random error to show that the summary point estimate is statistically significant at a pre-specified alpha, even though no constituent study was statistically significant.

Sanofi’s lawyers did not cite to any case for the remarkable proposition they advanced, but they did cite the Reference Manual for Scientific Evidence (RMSE). Earlier in the brief, the defense cited to this work in its third edition (2011), and so I turned to the cited page (“RMSE at 361–362, fn76”) only to find the introduction to the chapter on survey research, with footnotes 1 through 6.

After a diligent search through the third edition, I could not find any other language remotely supportive of the assertion by Sanofi’s counsel. There are important discussions about how a poorly conducted meta-analysis, or a meta-analysis that was heavily weighted in a direction by a methodologically flawed study, could render an expert witness’s opinion inadmissible under Rule 702.[10] Indeed, the third edition has a more sustained discussion of meta-analysis under the heading “VI. What Methods Exist for Combining the Results of Multiple Studies,”[11] but nothing in that discussion comes close to supporting the remarkable assertion by defense counsel.

On a hunch, I checked the second edition of RMSE, published in the year 2000. There was indeed a footnote 76, on page 361, which discussed meta-analysis. The discussion comes in the midst of the superseded edition’s chapter on epidemiology. Nothing, however, in the text or in the cited footnote appears to support the defense’s contention about meta-analyses are appropriate only when each included clinical trial has independently reported a statistically significant result.

If this analysis is correct, the MDL court was fully justified in rejecting the defense argument that combining two statistically non-significant clinical trials to yield a statistically significant result was methodologically infirm. No cases were cited, and the Reference Manual does not support the contention. Furthermore, no statistical text or treatise on meta-analysis supports the Sanofi claim. Sanofi did not support its motion with any affidavits of experts on meta-analysis.

Now there were other arguments advanced in support of excluding David Madigan’s testimony. Indeed, there was a very strong methodological challenge to Madigan’s decision to include the two RCTs in his meta-analysis, other than those RCTs lack of statistical significance on the end point at issue. In the words of the Sanofi brief:

“Both TAX clinical trials examined two different treatment regimens, TAC (docetaxel in combination with doxorubicin and cyclophosphamide) versus FAC (5-fluorouracil in combination with doxorubicin and cyclophosphamide). Madigan Report at 18–19 ¶¶ 47–48. Dr. Madigan admitted that TAC is not Taxotere alone, Madigan Dep. 305:21–23 (Ex. B); however, he did not rule out doxorubicin or cyclophosphamide in his analysis. Madigan Dep. 284:4–12 (“Q. You can’t rule out other chemotherapies as causes of irreversible alopecia? … A. I can’t rule out — I do not know, one way or another, whether other chemotherapy agents cause irreversible alopecia.”).”[12]

Now unlike the statistical significance argument, this argument is rather straightforward and turns on the clinical heterogeneity of the two trials that seems to clearly point to the invalidity of a meta-analysis of them. Sanofi’s lawyers could have easily supported this point with statements from standard textbooks and non-testifying experts (but alas did not). Sanofi did support their challenge, however, with citations to an important litigation and Fifth Circuit precedent.[13]

This closer look at the actual challenge to David Madigan’s opinions suggests that Sanofi’s counsel may have diluted very strong arguments about heterogeneity in exposure variable, and in the outcome variable, by advancing what seems a very doubtful argument based upon the lack of statistical significance of the individual studies in the Madigan meta-analysis.

Sanofi advanced two very strong points, first about the irrelevant outcome variable definitions used by Madigan, and second about the complexity of Taxotere’s being used with other, and different, chemotherapeutic agents in each of the two trials that Madigan combined.[14] The MDL court addressed the first point in a perfunctory and ultimately unsatisfactory fashion, but did not address the second point at all.

Ultimately, the result was that Madigan was given a pass to offer extremely tenuous opinions in an MDL on causation. Given that Madigan has proffered tendentious opinions in the past, and has been characterized as “an expert on a mission,” whose opinions are “conclusion driven,”[15] the missteps in the briefing, and the MDL court’s abridgement of the gatekeeping process are regrettable. Also regrettable is that the merits or demerits of a Rule 702 challenge cannot be fairly evaluated from cursory, conclusory judicial decisions riddled with meaningless verbiage such as “the challenge goes to the weight and not the admissibility of the witness.” Access to the actual Rule 702 motion helped shed important light on the inadequacy of one point in the motion but also the complexity and fullness of the challenge that was not fully addressed in the MDL court’s decision. It is possible that a Reply or a Supplemental brief, or oral argument, may have filled in gaps, corrected errors, or modified the motion, and the above analysis missed some important aspect of what happened in the Taxotere MDL. If so, all the more reason that we need better judicial gatekeeping, especially when a decision can affect thousands of pending cases.[16]

[1]  In re Taxotere (Docetaxel) Prods. Liab. Litig., 2019 U.S. Dist. LEXIS 143642, at *13 (E.D. La. Aug. 23, 2019) [Op.]

[2]  Op. at *13-14.

[3]  “Judicial Dodgers – Weight not Admissibility” (May 28, 2020).

[4]  Milward v. Acuity Specialty Prods. Grp., Inc., 639 F.3d 11, 17-22 (1st Cir. 2011).

[5]  Op. at *13-14 (quoting and citing Milward, 639 F.3d at 17-22).

[6]  Memorandum in Support of Sanofi Defendants’ Motion to Exclude Expert Testimony of David Madigan, Ph.D., Document 6144, in In re Taxotere (Docetaxel) Prods. Liab. Litig. (E.D. La. Feb. 8, 2019) [Brief].

[7]  Brief at 2; see also Brief at 14 (restating without initially explaining why combining two statistically non-significant RCTs by meta-analysis would be unhelpful).

[8]  Brief at 16.

[9]  Brief at 17 (quoting from Madigan Dep. 256:14–15).

[10]  Michael D. Green, Michael Freedman, and Leon Gordis, “Reference Guide on Epidemiology,” at 581n.89, in Fed. Jud. Center, Reference Manual on Scientific Evidence (3d ed. 2011).

[11]  Id. at 606.

[12]  Brief at 14.

[13]  Brief at 14, citing Burst v. Shell Oil Co., C. A. No. 14–109, 2015 WL 3755953, at *7 (E.D. La. June 16, 2015) (Vance, J.) (quoting LeBlanc v. Chevron USA, Inc., 396 F. App’x 94, 99 (5th Cir. 2010)) (“[A] study that notes ‘that the subjects were exposed to a range of substances and then nonspecifically note[s] increases in disease incidence’ can be disregarded.”), aff’d, 650 F. App’x 170 (5th Cir. 2016). See “The One Percent Non-solution – Infante Fuels His Own Exclusion in Gasoline Leukemia Case” (June 25, 2015).

[14]  Brief at 14-16.

[15]  In re Accutane Litig., 2015 WL 753674, at *19 (N.J.L.Div., Atlantic Cty., Feb. 20, 2015), aff’d, 234 N.J. 340, 191 A.3d 560 (2018). See “Johnson of Accutane – Keeping the Gate in the Garden State” (Mar. 28, 2015); “N.J. Supreme Court Uproots Weeds in Garden State’s Law of Expert Witnesses” (Aug. 8, 2018).

[16]  Cara Salvatore, “Sanofi Beats First Bellwether In Chemo Drug Hair Loss MDL,” Law360 (Sept. 27, 2019).

Posted in Meta-analysis, Rule 702, Rule 703, statistical evidence | Comments Off on David Madigan’s Graywashed Meta-Analysis in Taxotere MDL

Dodgy Data Duck Daubert Decisions

March 11th, 2020

Judges say the darndest things, especially when it comes to their gatekeeping responsibilities under Federal Rules of Evidence 702 and 703. One of the darndest things judges say is that they do not have to assess the quality of the data underlying an expert witness’s opinion.

Even when acknowledging their obligation to “assess the reasoning and methodology underlying the expert’s opinion, and determine whether it is both scientifically valid and applicable to a particular set of facts,”[1] judges have excused themselves from having to look at the trustworthiness of the underlying data for assessing the admissibility of an expert witness’s opinion.

In McCall v. Skyland Grain LLC, the defendant challenged an expert witness’s reliance upon oral reports of clients. The witness, Mr. Bradley Walker, asserted that he regularly relied upon such reports, in similar contexts of the allegations that the defendant misapplied herbicide to plaintiffs’ crops. The trial court ruled that the defendant could cross-examine the declarant who was available trial, and concluded that the “reliability of that underlying data can be challenged in that manner and goes to the weight to be afforded Mr. Walker’s conclusions, not their admissibility.”[2] Remarkably, the district court never evaluated the reasonableness of Mr. Walker’s reliance upon client reports in this or any context.

In another federal district court case, Rodgers v. Beechcraft Corporation, the trial judge explicitly acknowledged the responsibility to assess whether the expert witness’s opinion was based upon “sufficient facts and data,” but disclaimed any obligation to assess the quality of the underlying data.[3] The trial court in Rodgers cited a Tenth Circuit case from 2005,[4] which in turn cited the Supreme Court’s 1993 decision in Daubert, for the proposition that the admissibility review of an expert witness’s opinion was limited to a quantitative sufficiency analysis, and precluded a qualitative analysis of the underlying data’s reliability. Quoting from another district court criminal case, the court in Rodgers announced that “the Court does not examine whether the facts obtained by the witness are themselves reliable – whether the facts used are qualitatively reliable is a question of the weight to be given the opinion by the factfinder, not the admissibility of the opinion.”[5]

In a 2016 decision, United States v. DishNetwork LLC, the court explicitly disclaimed that it was required to “evaluate the quality of the underlying data or the quality of the expert’s conclusions.”[6] This district court pointed to a Seventh Circuit decision, which maintained that  “[t]he soundness of the factual underpinnings of the expert’s analysis and the correctness of the expert’s conclusions based on that analysis are factual matters to be determined by the trier of fact, or, where appropriate, on summary judgment.”[7] The Seventh Circuit’s decision, however, issued in June 2000, several months before the effective date of the amendments to Federal Rule of Evidence 702 (December 2000).

In 2012, a magistrate judge issued an opinion along the same lines, in Bixby v. KBR, Inc.[8] After acknowledging what must be done in ruling on a challenge to an expert witness, the judge took joy in what could be overlooked. If the facts or data upon which the expert witness has relied are “minimally sufficient,” then the gatekeeper can regard questions about “the nature or quality of the underlying data bear upon the weight to which the opinion is entitled or to the credibility of the expert’s opinion, and do not bear upon the question of admissibility.”[9]

There need not be any common law mysticism to the governing standard. The relevant law is, of course, a statute, which appears to be forgotten in many of the failed gatekeeping decisions:

Rule 702. Testimony by Expert Witnesses

A witness who is qualified as an expert by knowledge, skill, experience, training, or education may testify in the form of an opinion or otherwise if:

(a) the expert’s scientific, technical, or other specialized knowledge will help the trier of fact to understand the evidence or to determine a fact in issue;

(b) the testimony is based on sufficient facts or data;

(c) the testimony is the product of reliable principles and methods; and

(d) the expert has reliably applied the principles and methods to the facts of the case.

It would seem that you could not produce testimony that is the product of reliable principles and methods by starting with unreliable underlying facts and data. Certainly, having a reliable method would require selecting reliable facts and data from which to start. What good would the reliable application of reliable principles to crummy data?

The Advisory Committee Notes to Rule 702 hints at an answer to the problem:

“There has been some confusion over the relationship between Rules 702 and 703. The amendment makes clear that the sufficiency of the basis of an expert’s testimony is to be decided under Rule 702. Rule 702 sets forth the overarching requirement of reliability, and an analysis of the sufficiency of the expert’s basis cannot be divorced from the ultimate reliability of the expert’s opinion. In contrast, the ‘reasonable reliance’ requirement of Rule 703 is a relatively narrow inquiry. When an expert relies on inadmissible information, Rule 703 requires the trial court to determine whether that information is of a type reasonably relied on by other experts in the field. If so, the expert can rely on the information in reaching an opinion. However, the question whether the expert is relying on a sufficient basis of information—whether admissible information or not—is governed by the requirements of Rule 702.”

The answer is only partially satisfactory. First, if the underlying data are independently admissible, then there may indeed be no gatekeeping of an expert witness’s reliance upon such data. Rule 703 imposes a reasonableness test for reliance upon inadmissible underlying facts and data, but appears to give otherwise admissible facts and data a pass. Second, the above judicial decisions do not mention any Rule 703 challenge to the expert witnesses’ reliance. If so, then there is a clear lesson for counsel. When framing a challenge to the admissibility of an expert witness’s opinion, show that the witness has unreasonably relied upon facts and data, from whatever source, in violation of Rule 703. Then show that without the unreasonably relied upon facts and data, the witness cannot show that his or her opinion satisfies Rule 702(a)-(d).

[1]  See, e.g., McCall v. Skyland Grain LLC, Case 1:08-cv-01128-KHV-BNB, Order (D. Colo. June 22, 2010) (Brimmer, J.) (citing Dodge v. Cotter Corp., 328 F.3d 1212, 1221 (10th Cir. 2003), citing in turn Daubert v. Merrill Dow Pharms., Inc., 509 U.S. 579,  592-93 (1993).

[2]  McCall v. Skyland Grain LLC Case 1:08-cv-01128-KHV-BNB, Order at p.9 n.6 (D. Colo. June 22, 2010) (Brimmer, J.)

[3]  Rodgers v. Beechcraft Corp., Case No. 15-CV-129-CVE-PJC, Report & Recommendation at p.6 (N.D. Okla. Nov. 29, 2016).

[4]  Id., citing United.States. v. Lauder, 409 F.3d 1254, 1264 (10th Cir. 2005) (“By its terms, the Daubert opinion applies only to the qualifications of an expert and the methodology or reasoning used to render an expert opinion” and “generally does not, however, regulate the underlying facts or data that an expert relies on when forming her opinion.”), citing Daubert v. Merrill Dow Pharms., Inc., 509 U.S. 579, 592-93 (1993).

[5]  Id., citing and quoting United States v. Crabbe, 556 F. Supp. 2d 1217, 1223
(D. Colo. 2008) (emphasis in original). In Crabbe, the district judge mostly excluded the challenged expert witness, thus rendering its verbiage on quality of data as obiter dicta). The pronouncements about the nature of gatekeeping proved harmless error when the court dismissed the case on other grounds. Rodgers v. Beechcraft Corp., 248 F. Supp. 3d 1158 (N.D. Okla. 2017) (granting summary judgment).

[6]  United States v. DishNetwork LLC, No. 09-3073, Slip op. at 4-5 (C.D. Ill. Jan. 13, 2016) (Myerscough, J.)

[7]  Smith v. Ford Motor Co., 215 F.3d 713, 718 (7th Cir. 2000).

[8]  Bixby v. KBR, Inc., Case 3:09-cv-00632-PK, Slip op. at 6-7 (D. Ore. Aug. 29, 2012) (Papak, M.J.)

[9]  Id. (citing Hangarter v. Provident Life & Accident Ins. Co., 373 F.3d 998, 1017 (9th Cir. 2004), quoting Children’s Broad Corp. v. Walt Disney Co., 357 F.3d 860, 865 (8th Cir. 2004) (“The factual basis of an expert opinion goes to the credibility of the testimony, not the admissibility, and it is up to the opposing party to examine the factual basis for the opinion in cross-examination.”).

Posted in Evidence, Expert Witnesses, Rule 702, Rule 703, Underlying Data | Comments Off on Dodgy Data Duck Daubert Decisions

Science Bench Book for Judges

July 13th, 2019

On July 1st of this year, the National Judicial College and the Justice Speakers Institute, LLC released an online publication of the Science Bench Book for Judges [Bench Book]. The Bench Book sets out to cover much of the substantive material already covered by the Federal Judicial Center’s Reference Manual:

Acknowledgments

Table of Contents

  1. Introduction: Why This Bench Book?
  2. What is Science?
  3. Scientific Evidence
  4. Introduction to Research Terminology and Concepts
  5. Pre-Trial Civil
  6. Pre-trial Criminal
  7. Trial
  8. Juvenile Court
  9. The Expert Witness
  10. Evidence-Based Sentencing
  11. Post Sentencing Supervision
  12. Civil Post Trial Proceedings
  13. Conclusion: Judges—The Gatekeepers of Scientific Evidence

Appendix 1 – Frye/Daubert—State-by-State

Appendix 2 – Sample Orders for Criminal Discovery

Appendix 3 – Biographies

The Bench Book gives some good advice in very general terms about the need to consider study validity,[1] and to approach scientific evidence with care and “healthy skepticism.”[2] When the Bench Book attempts to instruct on what it represents the scientific method of hypothesis testing, the good advice unravels:

“A scientific hypothesis simply cannot be proved. Statisticians attempt to solve this dilemma by adopting an alternate [sic] hypothesis – the null hypothesis. The null hypothesis is the opposite of the scientific hypothesis. It assumes that the scientific hypothesis is not true. The researcher conducts a statistical analysis of the study data to see if the null hypothesis can be rejected. If the null hypothesis is found to be untrue, the data support the scientific hypothesis as true.”[3]

Even in experimental settings, a statistical analysis of the data do not lead to a conclusion that the null hypothesis is untrue, as opposed to not reasonably compatible with the study’s data. In observational studies, the statistical analysis must acknowledge whether and to what extent the study has excluded bias and confounding. When the Bench Book turns to speak of statistical significance, more trouble ensues:

“The goal of an experiment, or observational study, is to achieve results that are statistically significant; that is, not occurring by chance.”[4]

In the world of result-oriented science, and scientific advocacy, it is perhaps true that scientists seek to achieve statistically significant results. Still, it seems crass to come right out and say so, as opposed to saying that the scientists are querying the data to see whether they are compatible with the null hypothesis. This first pass at statistical significance is only mildly astray compared with the Bench Book’s more serious attempts to define statistical significance and confidence intervals:

“4.10 Statistical Significance

The research field agrees that study outcomes must demonstrate they are not the result of random chance. Leaving room for an error of .05, the study must achieve a 95% level of confidence that the results were the product of the study. This is denoted as p ≤ 05. (or .01 or .1).”[5]

and

“The confidence interval is also a way to gauge the reliability of an estimate. The confidence interval predicts the parameters within which a sample value will fall. It looks at the distance from the mean a value will fall, and is measured by using standard deviations. For example, if all values fall within 2 standard deviations from the mean, about 95% of the values will be within that range.”[6]

Of course, the interval speaks to the precision of the estimate, not its reliability, but that is a small point. These definitions are virtually guaranteed to confuse judges into conflating statistical significance and the coefficient of confidence with the legal burden of proof probability.

The Bench Book runs into problems in interpreting legal decisions, which would seem softer grist for the judicial mill. The authors present dictum from the Daubert decision as though it were a holding:[7]

“As noted in Daubert, ‘[t]he focus, of course, must be solely on principles and methodology, not on the conclusions they generate’.”

The authors fail to mention that this dictum was abandoned in Joiner, and that it is specifically rejected by statute, in the 2000 revision to the Federal Rule of Evidence 702.

Early in the Bench Book, it authors present a subsection entitled “The Myth of Scientific Objectivity,” which they might have borrowed from Feyerabend or Derrida. The heading appears misleading because the text contradicts it:

“Scientists often develop emotional attachments to their work—it can be difficult to abandon an idea. Regardless of bias, the strongest intellectual argument, based on accepted scientific hypotheses, will always prevail, but the road to that conclusion may be fraught with scholarly cul-de-sacs.”[8]

In a similar vein, the authors misleadingly tell readers that “the forefront of science is rarely encountered in court,” and so “much of the science mentioned there shall be considered established….”[9] Of course, the reality is that many causal claims presented in court have already been rejected or held to be indeterminate by the scientific community. And just when readers may think themselves safe from the goblins of nihilism, the authors launch into a theory of naïve probabilism that science is just placing subjective probabilities upon data, based upon preconceived biases and beliefs:

“All of these biases and beliefs play into the process of weighing data, a critical aspect of science. Placing weight on a result is the process of assigning a probability to an outcome. Everything in the universe can be expressed in probabilities.”[10]

So help the expert witness who honestly (and correctly) testifies that the causal claim or its rejection cannot be expressed as a probability statement!

Although I have not read all of the Bench Book closely, there appears to be no meaningful discussion of Rule 703, or of the need to access underlying data to ensure that the proffered scientific opinion under scrutiny has used appropriate methodologies at every step in its development. Even a 412 text cannot address every issue, but this one does little to help the judicial reader find more in-depth help on statistical and scientific methodological issues that arise in occupational and environmental disease claims, and in pharmaceutical products litigation.

The organizations involved in this Bench Book appear to be honest brokers of remedial education for judges. The writing of this Bench Book was funded by the State Justice Institute (SJI) Which is a creation of federal legislation enacted with the laudatory goal of improving the quality of judging in state courts.[11] Despite its provenance in federal legislation, the SJI is a a private, nonprofit corporation, governed by 11 directors appointed by the President, and confirmed by the Senate. A majority of the directors (six) are state court judges, one state court administrator, and four members of the public (no more than two from any one political party). The function of the SJI is to award grants to improve judging in state courts.

The National Judicial College (NJC) originated in the early 1960s, from the efforts of the American Bar Association, American Judicature Society and the Institute of Judicial Administration, to provide education for judges. In 1977, the NJC became a Nevada not-for-profit (501)(c)(3) educational corporation, which its campus at the University of Nevada, Reno, where judges could go for training and recreational activities.

The Justice Speakers Institute appears to be a for-profit company that provides educational resources for judge. A Press Release touts the Bench Book and follow-on webinars. Caveat emptor.

The rationale for this Bench Book is open to question. Unlike the Reference Manual for Scientific Evidence, which was co-produced by the Federal Judicial Center and the National Academies of Science, the Bench Book’s authors are lawyers and judges, without any subject-matter expertise. Unlike the Reference Manual, the Bench Book’s chapters have no scientist or statistician authors, and it shows. Remarkably, the Bench Book does not appear to cite to the Reference Manual or the Manual on Complex Litigation, at any point in its discussion of the federal law of expert witnesses or of scientific or statistical method. Perhaps taxpayers would have been spared substantial expense if state judges were simply encouraged to read the Reference Manual.

[1]  Bench Book at 190.

[2]  Bench Book at 174 (“Given the large amount of statistical information contained in expert reports, as well as in the daily lives of the general society, the ability to be a competent consumer of scientific reports is challenging. Effective critical review of scientific information requires vigilance, and some healthy skepticism.”).

[3]  Bench Book at 137; see also id. at 162.

[4]  Bench Book at 148.

[5]  Bench Book at 160.

[6]  Bench Book at 152.

[7]  Bench Book at 233, quoting Daubert v. Merrell Dow Pharms., Inc., 509 U.S. 579, 595 (1993).

[8]  Bench Book at 10.

[9]  Id. at 10.

[10]  Id. at 10.

[11] See State Justice Institute Act of 1984 (42 U.S.C. ch. 113, 42 U.S.C. § 10701 et seq.).

Posted in Causation, Evidence, Expert Witness Discovery, Expert Witnesses, Frye, Reference Manual on Scientific Evidence, Rule 702, Rule 703, Scientific Evidence, statistical evidence | Comments Off on Science Bench Book for Judges

Daubert Retrospective – Statistical Significance

January 5th, 2019

The holiday break was an opportunity and an excuse to revisit the briefs filed in the Supreme Court by parties and amici, in the Daubert case. The 22 amicus briefs in particular provided a wonderful basis upon which to reflect how far we have come, and also how far we have to go, to achieve real evidence-based fact finding in technical and scientific litigation. Twenty-five years ago, Rules 702 and 703 vied for control over errant and improvident expert witness testimony. With Daubert decided, Rule 702 emerged as the winner. Sadly, most courts seem to ignore or forget about Rule 703, perhaps because of its awkward wording. Rule 702, however, received the judicial imprimatur to support the policing and gatekeeping of dysepistemic claims in the federal courts.

As noted last week,1 the petitioners (plaintiffs) in Daubert advanced several lines of fallacious and specious argument, some of which was lost in the shuffle and page limitations of the Supreme Court briefings. The plaintiffs’ transposition fallacy received barely a mention, although it did bring forth at least a footnote in an important and overlooked amicus brief filed by American Medical Association (AMA), the American College of Physicians, and over a dozen other medical specialty organizations,2 all of which both emphasized the importance of statistical significance in interpreting epidemiologic studies, and the fallacy of interpreting 95% confidence intervals as providing a measure of certainty about the estimated association as a parameter. The language of these associations’ amicus brief is noteworthy and still relevant to today’s controversies.

The AMA’s amicus brief, like the brief filed by the National Academies of Science and the American Association for the Advancement of Science, strongly endorsed a gatekeeping role for trial courts to exclude testimony not based upon rigorous scientific analysis:

“The touchstone of Rule 702 is scientific knowledge. Under this Rule, expert scientific testimony must adhere to the recognized standards of good scientific methodology including rigorous analysis, accurate and statistically significant measurement, and reproducibility.”3

Having incorporated the term “scientific knowledge,” Rule 702 could not permit anything less in expert witness testimony, lest it pollute federal courtrooms across the land.

Elsewhere, the AMA elaborated upon its reference to “statistically significant measurement”:

“Medical researchers acquire scientific knowledge through laboratory investigation, studies of animal models, human trials, and epidemiological studies. Such empirical investigations frequently demonstrate some correlation between the intervention studied and the hypothesized result. However, the demonstration of a correlation does not prove the hypothesized result and does not constitute scientific knowledge. In order to determine whether the observed correlation is indicative of a causal relationship, scientists necessarily rely on the concept of “statistical significance.” The requirement of statistical reliability, which tends to prove that the relationship is not merely the product of chance, is a fundamental and indispensable component of valid scientific methodology.”4

And then again, the AMA spelled out its position, in case the Court missed its other references to the importance of statistical significance:

“Medical studies, whether clinical trials or epidemiologic studies, frequently demonstrate some correlation between the action studied … . To determine whether the observed correlation is not due to chance, medical scientists rely on the concept of ‘statistical significance’. A ‘statistically significant’ correlation is generally considered to be one in which statistical analysis suggests that the observed relationship is not the result of chance. A statistically significant correlation does not ‘prove’ causation, but in the absence of such a correlation, scientific causation clearly is not proven.9”5

In its footnote 9, in the above quoted section of the brief, the AMA called out the plaintiffs’ transposition fallacy, without specifically citing to plaintiffs’ briefs:

“It is misleading to compare the 95% confidence level used in empirical research to the 51% level inherent in the preponderance of the evidence standard.”6

Actually the plaintiffs’ ruse was much worse than misleading. The plaintiffs did not compare the two probabilities; they equated them. Some might call this ruse, an outright fraud on the court. In any event, the AMA amicus brief remains an available, citable source for opposing this fraud and the casual dismissal of the importance of statistical significance.

One other amicus brief touched on the plaintiffs’ statistical shanigans. The Product Liability Advisory Council, National Association of Manufacturers, Business Roundtable, and Chemical Manufacturers Association jointly filed an amicus brief to challenge some of the excesses of the plaintiffs’ submissions.7  Plaintiffs’ expert witness, Shanna Swan, had calculated type II error rates and post-hoc power for some selected epidemiologic studies relied upon by the defense. Swan’s complaint had been that some studies had only 20% probability (power) to detect a statistically significant doubling of limb reduction risk, with significance at p < 5%.8

The PLAC Brief pointed out that power calculations must assume an alternative hypothesis, and that the doubling of risk hypothesis had no basis in the evidentiary record. Although the PLAC complaint was correct, it missed the plaintiffs’ point that the defense had set exceeding a risk ratio of 2.0, as an important benchmark for specific causation attributability. Swan’s calculation of post-hoc power would have yielded an even lower probability for detecting risk ratios of 1.2 or so. More to the point, PLAC noted that other studies had much greater power, and that collectively, all the available studies would have had much greater power to have at least one study achieve statistical significance without dodgy re-analyses.

1 “The Advocates’ Errors in Daubert” (Dec. 28, 2018).

2 American Academy of Allergy and Immunology, American Academy of Dermatology, American Academy of Family Physicians, American Academy of Neurology, American Academy of Orthopaedic Surgeons, American Academy of Pain Medicine, American Association of Neurological Surgeons, American College of Obstetricians and Gynecologists, American College of Pain Medicine, American College of Physicians, American College of Radiology, American Society of Anesthesiologists, American Society of Plastic and Reconstructive Surgeons, American Urological Association, and College of American Pathologists.

3 Brief of the American Medical Association, et al., as Amici Curiae, in Support of Respondent, in Daubert v. Merrell Dow Pharmaceuticals, Inc., U.S. Supreme Court no. 92-102, 1993 WL 13006285, at *27 (U.S., Jan. 19, 1993)[AMA Brief].

4 AMA Brief at *4-*5 (emphasis added).

5 AMA Brief at *14-*15 (emphasis added).

6 AMA Brief at *15 & n.9.

7 Brief of the Product Liability Advisory Council, Inc., National Association of Manufacturers, Business Roundtable, and Chemical Manufacturers Association as Amici Curiae in Support of Respondent, as Amici Curiae, in Support of Respondent, in Daubert v. Merrell Dow Pharmaceuticals, Inc., U.S. Supreme Court no. 92-102, 1993 WL 13006288 (U.S., Jan. 19, 1993) [PLAC Brief].

8 PLAC Brief at *21.

Posted in Evidence, Expert Witnesses, Rule 702, Rule 703, statistical evidence, Statistical Power | Comments Off on Daubert Retrospective – Statistical Significance

Confounding in Daubert, and Daubert Confounded

November 4th, 2018

ABERRANT DECISIONS

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

Abilify

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).

4 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)

Posted in Causation, Expert Witnesses, Rule 702, Rule 703, Scientific Evidence | Comments Off on Confounding in Daubert, and Daubert Confounded

Rule 702 Requires Courts to Sort Out Confounding

October 31st, 2018

CONFOUNDING1

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.

Pre-Daubert

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

Post-Daubert

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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Daubert’s Silver Anniversary – Retrospective View of Its Friends and Enemies

October 21st, 2018

Science is inherently controversial because when done properly it has no respect for power or political correctness or dogma or entrenched superstition. We should thus not be surprised that the scientific process has many detractors in houses of worship, houses of representatives, and houses of the litigation industry. And we have more than a few “Dred Scott” decisions, in which courts have held that science has no criteria of validity that they are bound to follow.

To be sure, many judges have recognized a different danger in scientific opinion testimony, namely, its ability to overwhelm the analytical faculties of lay jurors. Fact-finders may view scientific expert witness opinion testimony as having an overwhelming certainty and authority, which swamps their ability to evaluate the testimony.1

One errant judicial strategy to deal with their own difficulty in evaluating scientific evidence was to invent a fictitious divide between a scientific and legal burden of proof:2

“Petitioners demand sole reliance on scientific facts, on evidence that reputable scientific techniques certify as certain. Typically, a scientist will not so certify evidence unless the probability of error, by standard statistical measurement, is less than 5%. That is, scientific fact is at least 95% certain. Such certainty has never characterized the judicial or the administrative process. It may be that the ‘beyond a reasonable doubt’ standard of criminal law demands 95% certainty. Cf. McGill v. United States, 121 U.S.App. D.C. 179, 185 n.6, 348 F.2d 791, 797 n.6 (1965). But the standard of ordinary civil litigation, a preponderance of the evidence, demands only 51% certainty. A jury may weigh conflicting evidence and certify as adjudicative (although not scientific) fact that which it believes is more likely than not.”

By falsely elevating the scientific standard, judges see themselves free to decide expeditiously and without constraints, because they are operating at much lower epistemic level.

Another response advocated by “the Lobby,” scientists in service to the litigation industry, has been to deprecate gatekeeping altogether. Perhaps the most brazen anti-science response to the Supreme Court’s decision in Daubert was advanced by David Michaels and his Project on Scientific Knowledge and Public Policy (SKAPP). In its heyday, SKAPP organized meetings and conferences, and cranked out anti-gatekeeping propaganda to the delight of the litigation industry3, while obfuscating and equivocating about the source of its funding (from the litigation industry).4

SKAPP principal David Michaels was also behind the efforts of the American Public Health Association (APHA) to criticize the judicial move to scientific standards in gatekeeping. In 2004, Michaels and fellow litigation industrialists prevailed upon the APHA to adopt a policy statement that attacked evidence-based science and data transparency in the form of “Policy Number: 2004-11 Threats to Public Health Science.”5

SKAPP appears to have gone the way of the dodo, although the defunct organization still has a Wikipedia­ page with the misleading claim that a federal court had funded its operation, and the old link for this sketchy outfit now redirects to the website for the Union of Concerned Scientists. In 2009, David Michaels, fellow in the Collegium Ramazzini, and formerly the driving force of SKAPP, went on to become an under-secretary of Labor, and OSHA administrator in the Obama administration.6

With the end of his regulatory work, Michaels is now back in the litigation saddle. In April 2018, Michaels participated in a ruse in which he allowed himself to be “subpoenaed” by Mark Lanier, to give testimony in a cases involving claims that personal talc use caused ovarian cancers.7 Michaels had no real subject matter expertise, but he readily made himself available so that Mr. Lanier could inject Michaels’ favorite trope of “doubt is their product” into his trial.

Against this backdrop of special pleading from the litigation industry’s go-to expert witnesses, it is helpful to revisit the Daubert decision, which is now 25 years old. The decision followed the grant of the writ of certiorari by the Supreme Court, full briefing by the parties on the merits, oral argument, and twenty two amicus briefs. Not all briefs are created equal, and this inequality is especially true of amicus briefs, for which the quality of argument, and the reputation of the interested third parties, can vary greatly. Given the shrill ideological ranting of SKAPP and the APHA, we might find some interest in what two leading scientific organizations, the American Association for the Advancement of Science (AAAS) and the National Academy of Science (NAS), contributed to the debate over the proper judicial role in policing expert witness opinion testimony.

The Amicus Brief of the AAAS and the NAS, filed in Daubert v. Merrell Dow Pharmaceuticals, Inc., U.S. Supreme Court No. 92-102 (Jan. 19, 1993), was submitted by Richard A. Meserve and Lars Noah, of Covington & Burling, and by Bert Black, of Weinberg & Green. Unfortunately, the brief does not appear to be available on Westlaw, but it was republished shortly after filing, at 12 Biotechnology Law Report 198 (No. 2, March-April 1993) [all citations below are to this republication].

The amici were and are well known to the scientific community. The AAAS is a not-for-profit scientific society, which publishes the prestigious journal Science, and engages in other activities to advance public understanding of science. The NAS was created by congressional charter in the administration of Abraham Lincoln, to examine scientific, medical, and technological issues of national significance. Brief at 208. Meserve, counsel of record for these Amici Curiae, is a member of the National Academy, a president emeritus of the Carnegie Institution for Science, and a former chair of the U.S. Nuclear Regulatory Commission. He received his doctorate in applied physics from Stanford University, and his law degree from Harvard. Noah is now a professor of law in the University of Florida, and Black is still a practicing lawyer, ironically for the litigation industry.

The brief of the AAAP and the NAS did not take a position on the merits of whether Bendectin can cause birth defects, but it had a great deal to say about the scientific process, and the need for courts to intervene to ensure that expert witness opinion testimony was developed and delivered with appropriate methodological rigor.

A Clear and Present Danger

The amici, AAAS and NAS, clearly recognized a threat to the integrity of scientific fact-finding in the regime of uncontrolled and unregulated expert witness testimony. The amici cited the notorious case of Wells v. Ortho Pharmaceutical Corp.8, which had provoked an outcry from the scientific community, and a particularly scathing article by two scientists from the National Institute of Child Health and Human Development.9

The amici also cited several judicial decisions on the need for robust gatekeeping, including the observations of Judge Jack Weinstein that

“[t]he uncertainty of the evidence in [toxic tort] cases, dependent as it is on speculative scientific hypotheses and epidemiological studies, creates a special need for robust screening of experts and gatekeeping under Rules 403 and 703 by the court.”10

The AAAS and the NAS saw the “obvious danger that research results generated solely for litigation may be skewed.” Brief at 217& n.11.11 The AAAS and the NAS thus saw a real, substantial threat in countenancing expert witnesess who proffered “putatively scientific evidence that does not in fact reflect the application of scientific principles.” Brief at 208. The Supreme Court probably did not need the AAAS and the NAS to tell them that “[s]uch evidence can lead to incorrect decisions and can serve to discredit the contributions of science,” id., but it may have helped ensure that the Court articulated meaningful guidelines to trial judges to police their courtrooms against scientific conclusions that were not reached in accordance with scientific principles. The amici saw and stated that

“[t]he unique persuasive power of scientific evidence and its inherent limitations requires that courts engage special efforts to ensure that scientific evidence is valid and reliable before it is admitted. In performing that task, courts can look to the same criteria that scientists themselves use to evaluate scientific claims.”

Brief at 212.

It may seem quaint to the post-modernists at the APHA, but the AAAS and the NAS were actually concerned “to avoid outcomes that are at odds with reality,” and they were willing to urge that “courts must exercise special care to assure that such evidence is based on valid and reliable scientific methodologies.” Brief at 209 (emphasis added). The amici also urged caution in allowing opinion testimony that conflicted with existing learning, and which had not been presented to the scientific community for evaluation. Brief at 218-19. In the words of the amici:

“Courts should admit scientific evidence only if it conforms to scientific standards and is derived from methods that are generally accepted by the scientific community as valid and reliable. Such a test promotes sound judicial decisionmaking by providing workable means for screening and assessing the quality of scientific expert testimony in advance of trial.”

Brief at 233. After all, part of the scientific process itself is weeding out false ideas.

Authority for Judicial Control

The AAAS and NAS and its lawyers gave their full support to Merrill Dow’s position that “courts have the authority and the responsibility to exclude expert testimony that is based upon unreliable or misapplied methodologies.” Brief at 209. The Federal Rules of Evidence, and Rules 702, 703, and 403 in particular, gave trial courts “ample authority for empowering courts to serve as gatekeepers.” Brief at 230. The amici argued what ultimately would become the law, that judicial control, in the spirit and text of the Federal Rules, of “[t]hreshold determinations concerning the admissibility of scientific evidence are necessary to ensure accurate decisions and to avoid unnecessary expenditures of judicial resources on collateral issues. Brief at 210. The AAAS and NAS further recommended that:

“Determinations concerning the admissibility of expert testimony based on scientific evidence should be made by a judge in advance of trial. Such judicial control is explicitly called for under Rule 104(a) of the Federal Rules of Evidence, and threshold admissibility determinations by a judge serve several important functions, including simplification of issues at trial (thereby increasing the speed of trial), improvement in the consistency and predictability of results, and clarification of the issues for purposes of appeal. Indeed, it is precisely because a judge can evaluate the evidence in a focused and careful manner, free from the prejudices that might infect deliberations by a jury, that the determination should be made as a threshold matter.”

Brief at 228 (internal citations omitted).

Criteria of Validity

The AAAS and NAS did not shrink from the obvious implications of their position. They insisted that “[i]n evaluating scientific evidence, courts should consider the same factors that scientists themselves employ to assess the validity and reliability of scientific assertions.” Brief at 209, 210. The amici may have exhibited an aspirational view of the ability of judges, but they shared their optimistic view that “judges can understand the fundamental characteristics that separate good science from bad.” Brief at 210. Under the gatekeeping regime contemplated by the AAAS and the NAS, judges would have to think and analyze, rather than delegating to juries. In carrying out their task, judges would not be starting with a blank slate:

“When faced with disputes about expert scientific testimony, judges should make full use of the scientific community’s criteria and quality-control mechanisms. To be admissible, scientific evidence should conform to scientific standards and should be based on methods that are generally accepted by the scientific community as valid and reliable.”

Brief at 210. Questions such as whether an hypothesis has survived repeated severe, rigorous tests, whether the hypothesis is consistent with other existing scientific theories, whether the results of the tests have been presented to the scientific community, need to be answered affirmatively before juries are permitted to weigh in with their verdicts. Brief at 216, 217.

The AAAS and the NAS acknowledged implicitly and explicitly that courtrooms were not good places to trot out novel hypotheses, which lacked severe testing and sufficient evidentiary support. New theories must survive repeated testing and often undergo substantial refinements before they can be accepted in the scientific community. The scientific method requires nothing less. Brief at 219. These organizational amici also acknowledged that there will be occasionally “truly revolutionary advances” in the form of an hypothesis not fully tested. The danger of injecting bad science into broader decisions (such as encouraging meritless litigation, or the abandonment of useful products) should cause courts to view unestablished hypotheses with “heightened skepticism pending further testing and review.” Brief at 229. In other words, some hypotheses simply have not matured to the point at which they can support tort or other litigation.

The AAAS and the NAS contemplated that the gatekeeping process could and should incorporate the entire apparatus of scientific validity determinations into Rule 104(a) adjudications. Nowhere in their remarkable amicus brief do they suggest that if there some evidence (however weak) favoring a causal claim, with nothing yet available to weigh against it, expert witnesses can declare that they have the “weight of the evidence” on their side, and gain a ticket to the courthouse door. The scientists at SKAPP, or now those at the Union for Concerned Scientists, prefer to brand gatekeeping as a trick to sell “doubt.” What they fail to realize is that their propaganda threatens both universalism and organized skepticism, two of the four scientific institutional norms, described by sociologist of science Robert K. Merton.12

1 United States v. Brown, 557 F.2d 541, 556 (6th Cir. 1977) (“Because of its apparent objectivity, an opinion that claims a scientific basis is apt to carry undue weight with the trier of fact”); United States v. Addison, 498 F.2d 741, 744 (D.C. Cir. 1974) (“scientific proof may in some instances assume a posture of mystic infallibility in the eyes of a jury of laymen”). Some people say that our current political morass reflects poorly on the ability of United States citizens to assess and evaluate evidence and claims to the truth.

2 See, e.g., Ethyl Corp. v. EPA, 541 F.2d 1, 28 n.58 (D.C. Cir.), cert. denied, 426 U.S. 941 (1976). See also “Rhetorical Strategy in Characterizing Scientific Burdens of Proof” (Nov. 15, 2014).

3 See, e.g., Project on Scientific Knowledge and Public Policy, “Daubert: The Most Influential Supreme Court Ruling You’ve Never Heard Of” (2003).

4 See, e.g., “SKAPP A LOT” (April 30, 2010); “Manufacturing Certainty” (Oct. 25, 2011);“David Michaels’ Public Relations Problem” (Dec. 2, 2011); “Conflicted Public Interest Groups” (Nov. 3, 2013).

7 Notes of Testimony by David Michaels, in Ingham v. Johnson & Johnson, Case No. 1522-CC10417-01, St. Louis Circuit Ct, Missouri (April 17, 2018).

8 788 F.2d 741, 744-45 (11th Cir.), cert. denied, 479 U.S. 950 (1986). Remarkably, consultants for the litigation industry have continued to try to “rehabilitate” the Wells decision. See “Carl Cranor’s Conflicted Jeremiad Against Daubert” (Sept. 23, 2018).

9 James L. Mills & Duane Alexander, “Teratogens and Litogens,” 315 New Engl. J. Med. 1234, 1235 (1986).

10 Brief at n. 31, citing In re Agent Orange Product Liab. Litig., 611 F. Supp. 1267, 1269 (E.D.N.Y. 1985), aff’d, 818 F.2d 187 (2th Cir. 1987), cert. denied, 487 U.S. 1234 (1988).

11 citing among other cases, Perry v. United States, 755 F.2d 888, 892 (11th Cir. 1985) (“A scientist who has a formed opinion as to the answer he is going to find before he even begins his research may be less objective than he needs to be in order to produce reliable scientific results.”).

12 Robert K. Merton, “The Normative Structure of Science,” in Robert K. Merton, The Sociology of Science: Theoretical and Empirical Investigations, chap. 13, at 267, 270 (1973).

Posted in Causation, Peer Review, Rule 702, Rule 703 | Comments Off on Daubert’s Silver Anniversary – Retrospective View of Its Friends and Enemies

The Expert Witness Who Put God on His Reference List

August 28th, 2018

“And you never ask questions
When God’s on your side”

                                Bob Dylan, “With God on Our Side” 1963.

Cases involving claims of personal injury have inspired some of the most dubious scientific studies in the so-called medical literature, but the flights of fancy in published papers are nothing compared with what is recorded in the annals of expert witness testimony. The weaker the medical claims, the more outlandish is the expert testimony proffered. Claims for personal injury supposedly resulting from mold exposure are no exception to the general rule. The expert witness opinion testimony in mold litigation has resulted in several commentaries1 and professional position papers,2 offered to curb the apparent excesses.

Ritchie Shoemaker, M.D., has been a regular expert witness for the mold lawsuit industry. Professional criticism has not deterred Shoemaker, although discerning courts have put the kibosh on some of Shoemaker’s testimonial adventures.3

Shoemaker cannot be everywhere, and so in conjunction with the mold lawsuit industry, Shoemaker has taken to certifying new expert witnesses. But how will Shoemaker and his protégées overcome the critical judicial reception?

Enter Divine Intervention

“Make thee an ark of gopher wood; rooms shalt thou make in the ark, and shalt pitch it within and without with pitch.4”

Some say the age of prophets, burning bushes, and the like is over, but perhaps not so. Maybe God speaks to expert witnesses to fill in the voids left by missing evidence. Consider the testimony of Dr. Scott W. McMahon, who recently testified that he was Shoemaker trained, and divinely inspired:

Q. Jumping around a little bit, Doctor, how did your interest in indoor environmental quality in general, and mold in particular, how did that come about?

A. I had — in 2009, I had been asked to give a talk at a medical society at the end of October and the people who were involved in it were harassing me almost on a weekly basis asking me what the title of my talk was going to be. I had spoken to the same society the previous four years. I had no idea what I was going to speak about. I am a man of faith, I’ve been a pastor and a missionary and other things, so I prayed about it and what I heard in my head verbatim was pediatric mold exposure colon the next great epidemic question mark. That’s what I heard in my head. And so because I try to live by faith, I typed that up as an email and said this is the name of my topic. And then I said, okay, God, you have ten weeks to teach me about this, and he did. Within three, four weeks maybe five, he had connected me to Dr. Shoemaker who was the leading person in the world at that time and the discoverer of this chronic inflammatory response.

*****

I am a man of faith, I’ve been a pastor and everything. And I realized that this was a real entity.

*****

Q. And do you attribute your decision or the decision for you to start Whole World Health Care also to be a divine intervention?

A. Well, that certainly started the process but I used my brain, too. Like I said, I went and I investigated Dr. Shoemaker, I wanted to make sure that his methods were real, that he wasn’t doing, you know, some sort of voodoo medicine and I saw that he wasn’t, that his scientific practice was standard. I mean, he changes one variable at a time in tests. He tested every step of the way. And I found that his conclusions were realistic. And then, you know, over the last few years, I’ve 1 gathered my own data and I see that they confirm almost every one of his conclusions.

Q. Doctor, was there anything in your past or anything dealing with your family in terms of exposure to mold or other indoor health issues?

A. No, it was totally off my radar.

Q. *** I’m not going to go into great detail with respect to Dr. Shoemaker, but are you Shoemaker certified?

A. I am.

Deposition transcript of Dr. Scott W. McMahon, at pp.46-49, in Courcelle v. C.W. Nola Properties LLC, Orleans Parish, Louisiana No. 15-3870, Sec. 7, Div. F. (May 18, 2018).

You may be surprised that the examining lawyer did not ask about the voice in which God spoke. The examining lawyer seems to have accepted without further question that the voice was that of an adult male voice. Still did the God entity speak in English, or in tongues? Was it a deep, resonant voice like Morgan Freeman’s in Bruce Almighty (2003)? Or was it a Yiddische voice like George Burns, in Oh God (1977)? Were there bushes burning when God spoke to McMahon? Or did the toast burn darker than expected?

Some might think that McMahon was impudent if not outright blasphemous for telling God that “He” had 10 weeks in which to instruct McMahon in the nuances of how mold causes human illness. Apparently, God was not bothered by this presumptuousness and complied with McMahon, which makes McMahon a special sort of prophet.

Of course, McMahon says he used his “brain,” in addition to following God’s instructions. But really why bother? Were there evidentiary or inferential gaps filled in by the Lord? The deposition does not address this issue.

In federal court, and in many state courts, an expert witness may base opinions on facts or data that are not admissible if, and only if, expert witnesses “in the particular field would reasonably rely on those kinds of facts or data in forming an opinion on the subject.5”

Have other expert witnesses claimed divine inspiration for opinion testimony? A quick Pubmed search does not reveal any papers by God, or papers with God as someone’s Co-Author. It is only a matter of time, however, before a judge, some where, takes judicial notice of divinely inspired expert witness testimony.

1 See, e.g., Howard M. Weiner, Ronald E. Gots, and Robert P. Hein, “Medical Causation and Expert Testimony: Allergists at this Intersection of Medicine and Law,” 12 Curr. Allergy Asthma Rep. 590 (2012).

2 See, e.g., Bryan D. Hardin, Bruce J. Kelman, and Andrew Saxon, “ACOEM Evidence-Based Statement: Adverse Human Health Effects Associated with Molds in the Indoor Environment,” 45 J. Occup. & Envt’l Med. 470 (2003).

3 See, e.g., Chesson v. Montgomery Mutual Insur. Co., 434 Md. 346, 75 A.3d 932, 2013 WL 5311126 (2013) (“Dr. Shoemaker’s technique, which reflects a dearth of scientific methodology, as well as his causal theory, therefore, are not shown to be generally accepted in the relevant scientific community.”); Young v. Burton, 567 F. Supp. 2d 121, 130-31 (D.D.C. 2008) (excluding Dr. Shoemaker’s theories as lacking general acceptance and reliability; listing Virginia, Florida, and Alabama as states in which courts have rejected Shoemaker’s theory).

4 Genesis 6:14 (King James translation).

5 Federal Rule of Evidence. Bases of an Expert.

Posted in Expert Witness Hall of Shame, Expert Witnesses, Public Nuisances, Rule 703, Scientific Evidence | Comments Off on The Expert Witness Who Put God on His Reference List

Failed Gatekeeping in Ambrosini v. Labarraque (1996)

December 28th, 2017

The Ambrosini case straddled the Supreme Court’s 1993 Daubert decision. The case began before the Supreme Court clarified the federal standard for expert witness gatekeeping, and ended in the Court of Appeals for the District of Columbia, after the high court adopted the curious notion that scientific claims should be based upon reliable evidence and valid inferences. That notion has only slowly and inconsistently trickled down to the lower courts.

Given that Ambrosini was litigated in the District of Columbia, where the docket is dominated by regulatory controversies, frequently involving dubious scientific claims, no one should be surprised that the D.C. Court of Appeals did not see that the Supreme Court had read “an exacting standard” into Federal Rule of Evidence 702. And so, we see, in Ambrosini, this Court of Appeals citing and purportedly applying its own pre-Daubert decision in Ferebee v. Chevron Chem. Co., 552 F. Supp. 1297 (D.D.C. 1982), aff’d, 736 F.2d 1529 (D.C. Cir.), cert. denied, 469 U.S. 1062 (1984).1 In 2000, the Federal Rule of Evidence 702 was revised in a way that extinguishes the precedential value of Ambrosini and the broad dicta of Ferebee, but some courts and commentators have failed to stay abreast of the law.

Escolastica Ambrosini was using a synthetic progestin birth control, Depo-Provera, as well as an anti-nausea medication, Bendectin, when she became pregnant. The child that resulted from this pregnancy, Teresa Ambrosini, was born with malformations of her face, eyes, and ears, cleft lip and palate, and vetebral malformations. About three percent of all live births in the United States have a major malformation. Perhaps because the Divine Being has sovereign immunity, Escolastica sued the manufacturers of Bendectin and Depo-Provera, as well as the prescribing physician.

The causal claims were controversial when made, and they still are. The progestin at issue, medroxyprogesterone acetate (MPA), was embryotoxic in the cynomolgus monkey2, but not in the baboon3. The evidence in humans was equivocal at best, and involved mostly genital malformations4; the epidemiologic evidence for the MPA causal claim to this day remains unconvincing5.

At the close of discovery in Ambrosini, Upjohn (the manufacturer of the progestin) moved for summary judgment, with a supporting affidavit of a physician and geneticist, Dr. Joe Leigh Simpson. In his affidavit, Simpson discussed three epidemiologic studies, as well as other published papers, in support of his opinion that the progestin at issue did not cause the types of birth defects manifested by Teresa Ambrosini.

Ambrosini had disclosed two expert witnesses, Dr. Allen S. Goldman and Dr. Brian Strom. Neither Goldman nor Strom bothered to identify the papers, studies, data, or methodology used in arriving at an opinion on causation. Not surprisingly, the district judge was unimpressed with their opposition, and granted summary judgment for the defendant. Ambrosini v. Labarraque, 966 F.2d 1462, 1466 (D.C. Cir. 1992).

The plaintiffs appealed on the remarkable ground that Goldman’s and Strom’s crypto-evidence satisfied Federal Rule of Evidence 703. Even more remarkably, the Circuit, in a strikingly unscholarly opinion by Judge Mikva, opined that disclosure of relied-upon studies was not required for expert witnesses under Rules 703 and 705. Judge Mikva seemed to forget that the opinions being challenged were not given in testimony, but in (late-filed) affidavits that had to satisfy the requirement of Federal Rule of Civil Procedure 26. Id. at 1468-69. At trial, an expert witness may express an opinion without identifying its bases, but of course the adverse party may compel disclosure of those bases. In discovery, the proffered expert witness must supply all opinions and evidence relied upon in reach the opinions. In any event, the Circuit remanded the case for a hearing and further proceedings, at which the two challenged expert witnesses, Goldman and Strom, would have to identify the bases of their opinions. Id. at 1471.

Not long after the case landed back in the district court, the Supreme Court decided Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993). With an order to produce entered, plaintiffs’ counsel could no longer hide Goldman and Strom’s evidentiary bases, and their scientific inferences came under judicial scrutiny.

Upjohn moved again to exclude Goldman and Strom’s opinions. The district court upheld Upjohn’s challenges, and granted summary judgment in favor of Upjohn for the second time. The Ambrosinis appealed again, but the second case in the D.C. Circuit resulted in a split decision, with the majority holding that the exclusion of Goldman and Strom’s opinions under Rule 702 was erroneous. Ambrosini v. Labarraque, 101 F.3d 129 (D.C. Cir. 1996).

Although issued two decades ago, the majority’s opinion remains noteworthy as an example of judicial resistance to the existence and meaning of the Supreme Court’s Daubert opinion. The majority opinion uncritically cited the notorious Ferebee6 and other pre-Daubert decisions. The court embraced the Daubert dictum about gatekeeping being limited to methodologic consideration, and then proceeded to interpret methodology as superficially as necessary to sustain admissibility. If an expert witness claimed to have looked at epidemiologic studies, and epidemiology was an accepted methodology, then the opinion of the expert witness must satisfy the legal requirements of Daubert, or so it would seem from the opinion of the U.S. Court of Appeals for the District of Columbia.

Despite the majority’s hand waving, a careful reader will discern that there must have been substantial gaps and omissions in the explanations and evidence cited by plaintiffs’ expert witnesses. Seeing anything clearly in the Circuit’s opinion is made difficult, however, by careless and imprecise language, such as its descriptions of studies as showing, or not showing “causation,” when it could have meant only that such studies showed associations, with more or less random and systematic error.

Dr. Strom’s report addressed only general causation, and even so, he apparently did not address general causation of the specific malformations manifested by the plaintiffs’ child. Strom claimed to have relied upon the “totality of the data,” but his methodologic approach seems to have required him to dismiss studies that failed to show an association.

“Dr. Strom first set forth the reasoning he employed that led him to disagree with those studies finding no causal relationship [sic] between progestins and birth defects like Teresa’s. He explained that an epidemiologist evaluates studies based on their ‘statistical power’. Statistical power, he continued, represents the ability of a study, based on its sample size, to detect a causal relationship. Conventionally, in order to be considered meaningful, negative studies, that is, those which allege the absence of a causal relationship, must have at least an 80 to 90 percent chance of detecting a causal link if such a link exists; otherwise, the studies cannot be considered conclusive. Based on sample sizes too small to be reliable, the negative studies at issue, Dr. Strom explained, lacked sufficient statistical power to be considered conclusive.”

Id. at 1367.

Putting aside the problem of suggesting that an observational study detects a “causal relationship,” as opposed to an association in need of further causal evaluation, the Court’s précis of Strom’s testimony on power is troublesome, and typical of how other courts have misunderstood and misapplied the concept of statistical power. Statistical power is a probability of observing an association of a specified size at a specified level of statistical significance. The calculation of statistical power turns indeed on sample size, the level of significance probability preselected for “statistical significance, an assumed probability distribution of the sample, and, critically, an alternative hypothesis. Without a specified alternative hypothesis, the notion of statistical power is meaningless, regardless of what probability (80% or 90% or some other percentage) is sought for finding the alternative hypothesis. Furthermore, the notion that the defense must adduce studies with “sufficient statistical power to be considered conclusive” creates an unscientific standard that can never be met, while subverting the law’s requirement that the claimant establish causation.

The suggestion that the studies that failed to find an association cannot be considered conclusive because they “lacked sufficient statistical power” is troublesome because it distorts and misapplies the very notion of statistical power. No attempt was made to describe the confidence intervals surrounding the point estimates of the null studies; nor was there any discussion whether the studies could be aggregated to increase their power to rule out meaningful associations.

The Circuit court’s scientific jurisprudence was thus seriously flawed. Without a discussion of the end points observed, the relevant point estimates of risk ratios, and the confidence intervals, the reader cannot assess the strength of the claims made by Goldman and Strom, or by defense expert Simpson, in their reports. Without identifying the study endpoints, the reader cannot evaluate whether the plaintiffs’ expert witnesses relied upon relevant outcomes in formulating their opinions. The court viewed the subject matter from 30,000 feet, passing over at 600 mph, without engagement or care. A strong dissent, however, suggested serious mischaracterizations of the plaintiffs’ evidence by the majority.

The only specific causation testimony to support plaintiff’s claims came from Goldman, in what appears to have been a “differential etiology.” Goldman purported to rule out a genetic cause, even though he had not conducted a critical family history or ordered a state-of-the-art chromosomal study. Id. at 140. Of course, nothing in a differential etiology approach would allow a physician to rule out “unknown” causes, which, for birth defects, make up the most prevalent and likely causes to explain any particular case. The majority acknowledged that these were short comings, but rhetorically characterized them as substantive, not methodologic, and therefore as issues for cross-examination, not for consideration by a judicial gatekeeping. All this is magical thinking, but it continues to infect judicial approaches to specific causation. See, e.g., Green Mountain Chrysler Plymouth Dodge Jeep v. Crombie, 508 F. Supp. 2d 295, 311 (D.Vt. 2007) (citing Ambrosini for the proposition that “the possibility of uneliminated causes goes to weight rather than admissibility, provided that the expert has considered and reasonably ruled out the most obvious”). In Ambrosini, however, Dr. Goldman had not ruled out much of anything.

Circuit Judge Karen LeCraft Henderson dissented in a short, but pointed opinion that carefully marshaled the record evidence. Drs. Goldman and Strom had relied upon a study by Greenberg and Matsunaga, whose data failed to show a statistically significant association between MPA and cleft lip and palate, when the crucial issue of timing of exposure was taken into consideration. Ambrosini, 101 F.3d at 142.

Beyond the specific claims and evidence, Judge Henderson anticipated the subsequent Supreme Court decisions in Joiner, Kumho Tire, and Weisgram, and the year 2000 revision of Rule 702, in noting that the majority’s acceptance of glib claims to have used a “traditional methodology” would render Daubert nugatory. Id. at 143-45 (characterizing Strom and Goldman’s methodologies as “wispish”). Even more importantly, Judge Henderson refused to indulge the assumption that somehow the length of Goldman’s C.V. substituted for evidence that his methods satisfied the legal (or scientific) standard of reliability. Id.

The good news is that little or nothing in Ambrosini survives the 2000 amendment to Rule 702. The bad news is that not all federal judges seem to have noticed, and that some commentators continue to cite the case, as lovely.

Probably no commentator has promiscuously embraced Ambrosini as warmly as Carl Cranor, a philosopher, and occasional expert witness for the lawsuit industry, in several publications and presentations.8 Cranor has been particularly enthusiastic about Ambrosini’s approval of expert witness’s testimony that failed to address “the relative risk between exposed and unexposed populations of cleft lip and palate, or any other of the birth defects from which [the child] suffers,” as well as differential etiologies that exclude nothing.9 Somehow Cranor, as did the majority in Ambrosini, believes that testimony that fails to identify the magnitude of the point estimate of relative risk can “assist the trier of fact to understand the evidence or to determine a fact in issue.”10 Of course, without that magnitude given, the trier of fact could not evaluate the strength of the alleged association; nor could the trier assess the probability of individual causation to the plaintiff. Cranor also has written approvingly of lumping unrelated end points, which defeats the assessment of biological plausibility and coherence by the trier of fact. When the defense expert witness in Ambrosini adverted to the point estimates for relevant end points, the majority, with Cranor’s approval, rejected the null findings as “too small to be significant.”11 If the null studies were, in fact, too small to be useful tests of the plaintiffs’ claims, intellectual and scientific honesty required an acknowledgement that the evidentiary display was not one from which a reasonable scientist would draw a causal conclusion.

1Ambrosini v. Labarraque, 101 F.3d 129, 138-39 (D.C. Cir. 1996) (citing and applying Ferebee), cert. dismissed sub nom. Upjohn Co. v. Ambrosini, 117 S.Ct. 1572 (1997) See also David E. Bernstein, “The Misbegotten Judicial Resistance to the Daubert Revolution,” 89Notre Dame L. Rev. 27, 31 (2013).

2 S. Prahalada, E. Carroad, M. Cukierski, and A.G. Hendrickx, “Embryotoxicity of a single dose of medroxyprogesterone acetate (MPA) and maternal serum MPA concentrations in cynomolgus monkey (Macaca fascicularis),” 32 Teratology 421 (1985).

3 S. Prahalada, E. Carroad, and A.G. Hendrick, “Embryotoxicity and maternal serum concentrations of medroxyprogesterone acetate (MPA) in baboons (Papio cynocephalus),” 32 Contraception 497 (1985).

4 See, e.g., Z. Katz, M. Lancet, J. Skornik, J. Chemke, B.M. Mogilner, and M. Klinberg, “Teratogenicity of progestogens given during the first trimester of pregnancy,” 65 Obstet Gynecol. 775 (1985); J.L. Yovich, S.R. Turner, and R. Draper, “Medroxyprogesterone acetate therapy in early pregnancy has no apparent fetal effects,” 38 Teratology 135 (1988).

5 G. Saccone, C. Schoen, J.M. Franasiak, R.T. Scott, and V. Berghella, “Supplementation with progestogens in the first trimester of pregnancy to prevent miscarriage in women with unexplained recurrent miscarriage: a systematic review and meta-analysis of randomized, controlled trials,” 107 Fertil. Steril. 430 (2017).

6 Ferebee v. Chevron Chemical Co., 736 F.2d 1529, 1535 (D.C. Cir.), cert. denied, 469 U.S. 1062 (1984).

7 Dr. Strom was also quoted as having provided a misleading definition of statistical significance: “whether there is a statistically significant finding at greater than 95 percent chance that it’s not due to random error.” Ambrosini at 101 F.3d at 136. Given the majority’s inadequate description of the record, the description of witness testimony may not be accurate, and error cannot properly be allocated.

8 Carl F. Cranor, Toxic Torts: Science, Law, and the Possibility of Justice 320, 327-28 (2006); see also Carl F. Cranor, Toxic Torts: Science, Law, and the Possibility of Justice 238 (2d ed. 2016).

9 Carl F. Cranor, Toxic Torts: Science, Law, and the Possibility of Justice 320 (2006).

10 Id.

11 Id. ; see also Carl F. Cranor, Toxic Torts: Science, Law, and the Possibility of Justice 238 (2d ed. 2016).

Posted in Causation, Expert Witness Discovery, Rule 702, Rule 703, Scientific Evidence, statistical evidence, Statistical Power | Comments Off on Failed Gatekeeping in Ambrosini v. Labarraque (1996)

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