TORTINI

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

Federal Rules Get a Makeover

December 2nd, 2011

Bellbottoms are out; cuffs are in.  Robert Frost is out; Philip Levine is in.

So too with the Federal Rules.

The Federal Rules of Evidence have been “restyled.” Yesterday, the new, restyled Federal Rules of Evidence went into effect.

A PDF of the new rules is available at several places on the web, including the Federal Evidence Review website, which also has also links to the legislative history and guiding principles for this restyling.   The Legal Information Institute (LII) at Cornell Law School helpfully has posted ebooks, as ePub or mobi files, of the restyled Federal Rules of Civil Procedure, Criminal Procedure, and Evidence.

The legislative history of the restyled Evidence Rules 101-1103 make clear that the changes were designed to make the rules simpler, more readable and understandable, without changing their substantive meaning.  Was this effort worth the time and money?

The rules on expert witness opinion testimony are my particular interest.

Rule 703. Bases of an Expert’s Opinion Testimony

An expert may base an opinion on facts or data in the case that the expert has been made aware of or personally observed. If experts in the particular field would reasonably rely on those kinds of facts or data in forming an opinion on the subject, they need not be admissible for the opinion to be admitted. But if the facts or data would otherwise be inadmissible, the proponent of the opinion may disclose them to the jury only if their probative value in helping the jury evaluate the opinion substantially outweighs their prejudicial effect.

(Legislative History: Pub. L. 93-595, Jan. 2, 1975; Mar. 2, 1987, eff. Oct. 1, 1987; Apr. 17, 2000, eff. Dec. 1, 2000; Apr. 26, 2011, eff. Dec. 1, 2011.)

The rule specifies what happens “[i]f experts in the particular field would reasonably rely on those kinds of facts or data in forming an opinion on the subject,” but what happens “if not“?  The common reading interpolates “only” before “if,” but Rule 703 before and after restyling misses this drafting point.

So too does Rule 702:

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.

(Legislative History: Pub. L. 93-595, Jan. 2, 1975; Apr. 17, 2000, eff. Dec. 1, 2000; Apr. 26, 2011, eff. Dec. 1, 2011.)

And if not?

The enumeration of (a) through (d) in Rule 702, however, is an improvement for reading and comprehension, especially with the conjunction connecting the last member of the series.

I suppose at age 36, everyone is entitled to a makeover.

Epidemiology, Risk, and Causation – Report of Workshops

November 15th, 2011

This month’s issue of Preventive Medicine includes a series of papers arising from last year’s workshops on “Epidemiology, Risk, and Causation,” at Cambridge University. The workshops were organized by philosopher Alex Broadbent,  a member of the Department of History and Philosophy of Science, in Cambridge University.  The workshops were financially sponsored by the Foundation for Genomics and Population Health (PHG), a not-for-profit British organization.

Broadbent’s workshops were intended for philosophers of science, statisticians, and epidemiologists, lawyers involved in health effects litigation will find the papers of interest as well.  The themes of workshops included:

  • the nature of epidemiologic causation,
  • the competing claims of observational and experimental research for establishing causation,
  • the role of explanation and prediction in assessing causality,
  • the role of moral values in causal judgments, and
  • the role of statistical and epistemic uncertainty in causal judgments

See Alex Broadbent, ed., “Special Section: Epidemiology, Risk, and Causation,” 53 Preventive Medicine 213-356 (October-November 2011).  Preventive Medicine is published by Elsevier Inc., so you know that the articles are not free.  Still you may want to read these at your local library to determine what may be useful in challenging and defending causal judgments in the courtroom.  One of the interlocutors, Sander Greenland, is of particular interest because he shows up as an expert witness with some regularity.

Here are the individual papers published in this special issue:

Alfredo Morabia, Michael C. Costanza, Philosophy and epidemiology

Alex Broadbent, Conceptual and methodological issues in epidemiology: An overview

Alfredo Morabia, Until the lab takes it away from epidemiology

Nancy Cartwright, Predicting what will happen when we act. What counts for warrant?

Sander Greenland, Null misinterpretation in statistical testing and its impact on health risk assessment

Daniel M. Hausman, How can irregular causal generalizations guide practice

Mark Parascandola, Causes, risks, and probabilities: Probabilistic concepts of causation in chronic disease epidemiology

John Worrall, Causality in medicine: Getting back to the Hill top

Olaf M. Dekkers, On causation in therapeutic research: Observational studies, randomised experiments and instrumental variable analysis

Alexander Bird, The epistemological function of Hill’s criteria

Michael Joffe, The gap between evidence discovery and actual causal relationships

Stephen John, Why the prevention paradox is a paradox, and why we should solve it: A philosophical view

Jonathan Wolff, How should governments respond to the social determinants of health?

Alex Broadbent, What could possibly go wrong? — A heuristic for predicting population health outcomes of interventions, Pages 256-259

The Treatment of Meta-Analysis in the Third Edition of the Reference Manual on Scientific Evidence

November 14th, 2011

Meta-analysis is a statistical procedure for aggregating data and statistics from individual studies into a single summary statistical estimate of the population measurement of interest.  The first meta-analysis is typically attributed to Karl Pearson, circa 1904, who sought a method to overcome the limitations of small sample size and low statistical power.  Statistical methods for meta-analysis, however, did not mature until the 1970s.  Even then, the biomedical scientific community remained skeptical of, if not out rightly hostile to, meta-analysis until relatively recently.

The hostility to meta-analysis, especially in the context of observational epidemiologic studies, was colorfully expressed by Samuel Shapiro and Alvan Feinstein, as late as the 1990s:

“Meta-analysis begins with scientific studies….  [D]ata from these studies are then run through computer models of bewildering complexity which produce results of implausible precision.”

* * * *

“I propose that the meta-analysis of published non-experimental data should be abandoned.”

Samuel Shapiro, “Meta-analysis/Smeta-analysis,” 140 Am. J. Epidem. 771, 777 (1994).  See also Alvan Feinstein, “Meta-Analysis: Statistical Alchemy for the 21st Century,” 48 J. Clin. Epidem. 71 (1995).

The professional skepticism about meta-analysis was reflected in some of the early judicial assessments of meta-analysis in court cases.  In the 1980s and early 1990s, some trial judges erroneously dismissed meta-analysis as a flawed statistical procedure that claimed to make something out of nothing. Allen v. Int’l Bus. Mach. Corp., No. 94-264-LON, 1997 U.S. Dist. LEXIS 8016, at *71–*74 (suggesting that meta-analysis of observational studies was controversial among epidemiologists).

In In re Paoli Railroad Yard PCB Litigation, Judge Robert Kelly excluded plaintiffs’ expert witness Dr. William Nicholson and his testimony based upon his unpublished meta-analysis of health outcomes among PCB-exposed workers.  Judge Kelly found that the meta-analysis was a novel technique, and that Nicholson’s meta-analysis was not peer reviewed.  Furthermore, the meta-analysis assessed health outcomes not experienced by any of the plaintiffs before the trial court.  706 F. Supp. 358, 373 (E.D. Pa. 1988).

The Court of Appeals for the Third Circuit reversed the exclusion of Dr. Nicholson’s testimony, and remanded for reconsideration with instructions.  In re Paoli R.R. Yard PCB Litig., 916 F.2d 829, 856-57 (3d Cir. 1990), cert. denied, 499 U.S. 961 (1991); Hines v. Consol. Rail Corp., 926 F.2d 262, 273 (3d Cir. 1991).  The Circuit noted that meta-analysis was not novel, and that the lack of peer-review was not an automatic disqualification.  Acknowledging that a meta-analysis could be performed poorly using invalid methods, the appellate court directed the trial court to evaluate the validity of Dr. Nicholson’s work on his meta-analysis.

In one of many squirmishes over colorectal cancer claims in asbestos litigation, Judge Sweet in the Southern District of New York was unimpressed by efforts to aggregate data across studies.  Judge Sweet declared that “no matter how many studies yield a positive but statistically insignificant SMR for colorectal cancer, the results remain statistically insignificant. Just as adding a series of zeros together yields yet another zero as the product, adding a series of positive but statistically insignificant SMRs together does not produce a statistically significant pattern.”  In In re Joint E. & S. Dist. Asbestos Litig., 827 F. Supp. 1014, 1042 (S.D.N.Y. 1993).  The plaintiffs’ expert witness who had offered the unreliable testimony, Dr. Steven Markowitz, like Nicholson, another foot soldier in Dr. Irving Selikoff’s litigation machine, did not offer a formal meta-analysis to justify his assessment that multiple non-significant studies, taken together, rule out chance as a likely explanation for an aggregate finding of an increased risk.

Judge Sweet was quite justified in rejecting this back of the envelope, non-quantitative meta-analysis.  His suggestion, however, that multiple non-significant studies could never collectively serve to rule out chance as an explanation for an overall increased rate of disease in the exposed groups is wrong.  Judge Sweet would have better focused on the validity issues in key studies, the presence of bias and confounding, and the completeness of the proffered meta-analysis.  The Second Circuit reversed the entry of summary judgment, and remanded the colorectal cancer claim for trial.  52 F.3d 1124 (2d Cir. 1995).  Over a decade later, with even more accumulated studies and data, the Institute of Medicine found the evidence for asbestos plaintiffs’ colorectal cancer claims to be scientifically insufficient.  Institute of Medicine, Asbestos: Selected Cancers (Wash. D.C. 2006).

Courts continue to go astray with an erroneous belief that multiple studies, all without statistically significant results, cannot yield a statistically significant summary estimate of increased risk.  See, e.g., Baker v. Chevron USA, Inc., 2010 WL 99272, *14-15 (S.D.Ohio 2010) (addressing a meta-analysis by Dr. Infante on multiple myeloma outcomes in studies of benzene-exposed workers).  There were many sound objections to Infante’s meta-analysis, but the suggestion that multiple studies without statistical significance could not yield a summary estimate of risk with statistical significance was not one of them.

In the last two decades, meta-analysis has emerged as an important technique for addressing random variation in studies, as well as some of the limitations of frequentist statistical methods.  In 1980s, articles reporting meta-analyses were rare to non-existent.  In 2009, there were over 2,300 articles with “meta-analysis” in their title, or in their keywords, indexed in the PubMed database of the National Library of Medicine.  See Michael O. Finkelstein and Bruce Levin, “Meta-Analysis of ‘Sparse’ Data: Perspectives from the Avandia Cases” (2011) (forthcoming in Jurimetrics).

The techniques for aggregating data have been studied, refined, and employed extensively in thousands of methods and application papers in the last decade. Consensus guideline papers have been published for meta-analyses of clinical trials as well as observational studies.  See Donna Stroup, et al., “Meta-analysis of Observational Studies in Epidemiology: A Proposal for Reporting,” 283 J. Am. Med. Ass’n 2008 (2000) (MOOSE statement); David Moher, Deborah Cook, Susan Eastwood, Ingram Olkin, Drummond Rennie, and Donna Stroup, “Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement,” 354 Lancet 1896 (1999).  See also Jesse Berlin & Carin Kim, “The Use of Meta-Analysis in Pharmacoepidemiology,” in Brian Strom, ed., Pharmacoepidemiology 681, 683–84 (4th ed. 2005); Zachary Gerbarg & Ralph Horwitz, “Resolving Conflicting Clinical Trials: Guidelines for Meta-Analysis,” 41 J. Clin. Epidemiol. 503 (1988).

Meta-analyses, of observational studies and of randomized clinical trials, routinely are relied upon by expert witnesses in pharmaceutical and so-called toxic tort litigation. Id. See also In re Bextra and Celebrex Marketing Sales Practices and Prod. Liab. Litig., 524 F. Supp. 2d 1166, 1174, 1184 (N.D. Cal. 2007) (holding that reliance upon “[a] meta-analysis of all available published and unpublished randomized clinical trials” was reasonable and appropriate, and criticizing the expert witnesses who urged the complete rejection of meta-analysis of observational studies)

The second edition of the Reference Manual on Scientific Evidence gave very little attention to meta-analysis.  With this historical backdrop, it is interesting to see what the new third edition provides for guidance to the federal judiciary on this important topic.

STATISTICS CHAPTER

The statistics chapter of the third edition gives continues to give scant attention to meta-analysis.  The chapter notes, in a footnote, that there are formal procedures for aggregating data across studies, and that the power of the aggregated data will exceed the power of the individual, included studies.  The footnote then cautions that meta-analytic procedures “have their own weakness,” without detailing what that one weakness is.  RMSE 3d at 254 n. 107.

The glossary at the end of the statistics chapter offers a definition of meta-analysis:

“meta-analysis. Attempts to combine information from all studies on a certain topic. For example, in the epidemiological context, a meta-analysis may attempt to provide a summary odds ratio and confidence interval for the effect of a certain exposure on a certain disease.”

Id. at 289.

This definition is inaccurate in ways that could yield serious mischief.  Virtually all meta-analyses are built upon a systematic review that sets out to collect all available studies on a research issue of interest.  It is a rare meta-analysis, however, that includes “all” studies in its quantitative analysis.  The meta-analytic process involves a pre-specification of inclusionary and exclusionary criteria for the quantitative analysis of the summary estimate of risk.  Those criteria may limit the quantitative analysis to randomized trials, or to analytical epidemiologic studies.  Furthermore, meta-analyses frequently and appropriately have pre-specified exclusionary criteria that relate to study design or quality.

On a more technical note, the offered definition suggests that the summary estimate of risk will be an odds ratio, which may or may not be true.  Meta-analyses of risk ratios may yield summary estimates of risk in terms of relative risk or hazard ratios, or even of risk differences.  The meta-analysis may combine data of means rather than proportions as well.

EPIDEMIOLOGY CHAPTER

The chapter on epidemiology delves into meta-analysis in greater detail than the statistics chapter, and offers apparently inconsistent advice.  The overall gist of the chapter, however, can perhaps best be summarized by the definition offered in this chapter’s glossary:

“meta-analysis. A technique used to combine the results of several studies to enhance the precision of the estimate of the effect size and reduce the plausibility that the association found is due to random sampling error.  Meta-analysis is best suited to pooling results from randomly controlled experimental studies, but if carefully performed, it also may be useful for observational studies.”

Reference Guide on Epidemiology, RSME3d at 624.  See also id. at 581 n. 89 (“Meta-analysis is better suited to combining results from randomly controlled experimental studies, but if carefully performed it may also be helpful for observational studies, such as those in the epidemiologic field.”).  The epidemiology chapter appropriately notes that meta-analysis can help address concerns over random error in small studies.  Id. at 579; see also id. at 607 n. 171.

Having told us that properly conducted meta-analyses of observational studies can be helpful, the chapter hedges considerably:

“Meta-analysis is most appropriate when used in pooling randomized experimental trials, because the studies included in the meta-analysis share the most significant methodological characteristics, in particular, use of randomized assignment of subjects to different exposure groups. However, often one is confronted with nonrandomized observational studies of the effects of possible toxic substances or agents. A method for summarizing such studies is greatly needed, but when meta-analysis is applied to observational studies – either case-control or cohort – it becomes more controversial.174 The reason for this is that often methodological differences among studies are much more pronounced than they are in randomized trials. Hence, the justification for pooling the results and deriving a single estimate of risk, for example, is problematic.175

Id. at 607.  The stated objection to pooling results for observational studies is certainly correct, but many research topics have sufficient studies available to allow for appropriate selectivity in framing inclusionary and exclusionary criteria to address the objection.  The chapter goes on to credit the critics of meta-analyses of observational studies.  As they did in the second edition of the RSME, the authors repeat their cites to, and quotes from, early papers by John Bailar, who was then critical of such meta-analyses:

“Much has been written about meta-analysis recently and some experts consider the problems of meta-analysis to outweigh the benefits at the present time. For example, John Bailar has observed:

‘[P]roblems have been so frequent and so deep, and overstatements of the strength of conclusions so extreme, that one might well conclude there is something seriously and fundamentally wrong with the method. For the present . . . I still prefer the thoughtful, old-fashioned review of the literature by a knowledgeable expert who explains and defends the judgments that are presented. We have not yet reached a stage where these judgments can be passed on, even in part, to a formalized process such as meta-analysis.’

John Bailar, “Assessing Assessments,” 277 Science 528, 529 (1997).”

Id. at 607 n.177.  Bailar’s subjective preference for “old-fashioned” reviews, which often cherry picked the included studies is, well, “old fashioned.”  More to the point, it is questionable science, and a distinctly minority viewpoint in the light of substantial improvements in the conduct and reporting of meta-analyses of observational studies.  Bailar may be correct that some meta-analyses should have never left the protocol stage, but the RMSE 3d fails to provide the judiciary with the tools to appreciate the distinction between good and bad meta-analyses.

This categorical rejection, cited with apparent approval, is amplified by a recitation of some real or apparent problems with meta-analyses of observational studies.  What is missing is a discussion of how many of these problems can be and are dealt with in contemporary practice:

“A number of problems and issues arise in meta-analysis. Should only published papers be included in the meta-analysis, or should any available studies be used, even if they have not been peer reviewed? Can the results of the meta-analysis itself be reproduced by other analysts? When there are several meta-analyses of a given relationship, why do the results of different meta-analyses often disagree? The appeal of a meta-analysis is that it generates a single estimate of risk (along with an associated confidence interval), but this strength can also be a weakness, and may lead to a false sense of security regarding the certainty of the estimate. A key issue is the matter of heterogeneity of results among the studies being summarized.  If there is more variance among study results than one would expect by chance, this creates further uncertainty about the summary measure from the meta-analysis. Such differences can arise from variations in study quality, or in study populations or in study designs. Such differences in results make it harder to trust a single estimate of effect; the reasons for such differences need at least to be acknowledged and, if possible, explained.176 People often tend to have an inordinate belief in the validity of the findings when a single number is attached to them, and many of the difficulties that may arise in conducting a meta-analysis, especially of observational studies such as epidemiologic ones, may consequently be overlooked.177

Id. at 608.  The authors are entitled to their opinion, but their discussion leaves the judiciary uninformed about current practice, and best practices, in epidemiology.  A categorical rejection of meta-analyses of observational studies is at odds with the chapter’s own claim that such meta-analyses can be helpful if properly performed.  What was needed, and is missing, is a meaningful discussion to help the judiciary determine whether a meta-analysis of observational studies was properly performed.

MEDICAL TESTIMONY CHAPTER

The chapter on medical testimony is the third pass at meta-analysis in RMSE 3d.   The second edition’s chapter on medical testimony ignored meta-analysis completely; the new edition addresses meta-analysis in the context of the hierarchy of study designs:

“Other circumstances that set the stage for an intense focus on medical evidence included

(1) the development of medical research, including randomized controlled trials and other observational study designs;

(2) the growth of diagnostic and therapeutic interventions;141

(3) interest in understanding medical decision making and how physicians reason;142 and

(4) the acceptance of meta-analysis as a method to combine data from multiple randomized trials.143

RMSE 3d at 722-23.

The chapter curiously omits observational studies, but the footnote reference (note 143) then inconsistently discusses two meta-analyses of observational, rather than experimental, studies:

“143. Video Software Dealers Ass’n v. Schwarzenegger, 556 F.3d 950, 963 (9th Cir. 2009) (analyzing a meta-analysis of studies on video games and adolescent behavior); Kennecott Greens Creek Min. Co. v. Mine Safety & Health Admin., 476 F.3d 946, 953 (D.C. Cir. 2007) (reviewing the Mine Safety and Health Administration’s reliance on epidemiological studies and two meta-analyses).”

Id. at 723 n.143.

The medical testimony chapter then provides further confusion by giving a more detailed listing of the hierarchy of medical evidence in the form of different study designs:

3. Hierarchy of medical evidence

With the explosion of available medical evidence, increased emphasis has been placed on assembling, evaluating, and interpreting medical research evidence.  A fundamental principle of evidence-based medicine (see also Section IV.C.5, infra) is that the strength of medical evidence supporting a therapy or strategy is hierarchical.  When ordered from strongest to weakest, systematic review of randomized trials (meta-analysis) is at the top, followed by single randomized trials, systematic reviews of observational studies, single observational studies, physiological studies, and unsystematic clinical observations.150 An analysis of the frequency with which various study designs are cited by others provides empirical evidence supporting the influence of meta-analysis followed by randomized controlled trials in the medical evidence hierarchy.151 Although they are at the bottom of the evidence hierarchy, unsystematic clinical observations or case reports may be the first signals of adverse events or associations that are later confirmed with larger or controlled epidemiological studies (e.g., aplastic anemia caused by chloramphenicol,152 or lung cancer caused by asbestos153). Nonetheless, subsequent studies may not confirm initial reports (e.g., the putative association between coffee consumption and pancreatic cancer).154

Id. at 723-24.  This discussion further muddies the water by using a parenthetical to suggest that meta-analyses of randomized clinical trials are equivalent to systematic reviews of such studies — “systematic review of randomized trials (meta-analysis).” Of course, systematic reviews are not meta-analyses, although they are a necessary precondition for conducting a meta-analysis.  The relationship between the procedures for a systematic review and a meta-analysis are in need of clarification, but the judiciary will not find it in the new Reference Manual.

Lording the Data – Scientific Fraud

November 10th, 2011

Last week, the New York Times published a news story about psychologist Diederik Stapel, of the Netherlands.  Tilburg University accused him of having committed research fraud  in several dozen published papers, including the journal Science, the official journal of the AAAS.  See Benedict Carey, “Fraud Case Seen as a Red Flag for Psychology Research: Noted Dutch Psychologist, Stapel, Accused of Research Fraud,” New York Times (Nov. 2, 2011).  The Times expressed surprise over the suggestion that psychology is plagued by fraud and sloppy research.  The surprise is that there are not more stories in the lay media over the poor quality of scientific research.  The readers of Retraction Watch, and the Office of Research Integrity’s blog will recognize how commonplace Stapel’s fraud is.

Stapel’s fraud has wide-ranging implications for the doctoral students, whose dissertations he supervised, and for colleagues, with whom he collaborated.  Stapel apologized and expressed his regret, but his conduct leaves a large body of his work, and that of others, under a cloud of suspicion.

Lording the Data

The University committee reported that Stapel had escaped detection for a long time because he was “lord of the data,” by refusing to disclose and share the data.

“Outright fraud may be rare, these experts say, but they contend that Dr. Stapel took advantage of a system that allows researchers to operate in near secrecy and massage data to find what they want to find, without much fear of being challenged.”

Benedict Carey, “Fraud Case,” New York Times (Nov. 2, 2011).  Data sharing is preached but rarely practice.

In a recent publication, Dr. Wicherts and his colleagues, at the University of Amsterdam, reported that two-thirds of his sample of Dutch research psychologists refused to share their data, in contravention of the established ethical rules of the discipline. Remarkably, many of the refuseniks had explicit contractual obligations with their publishing journals to provide data.  Jelte Wicherts, Marjan Bakker, Dylan Molenaar, “Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results,” PLoS ONE 6(11): e26828 (Nov. 2, 2011)

Scientific fraud seems no more common among scientists with industry ties, which are so often the subject of ad hominem conflict of interest claims.  Instead, fraudfeasors such as Stapel or Hwang Woo-suk are more often simply egotistical, narcissistic, self-aggrandizing, self-promoting, or delusional.  In the United States, litigation, occasionally has brought out charlatans, but it has also resulted in high-quality studies that have provided strong evidence for or against litigation claims.  Compare Hon. Jack B. Weinstein, “Preliminary Reflections on Administration of Complex Litigation” 2009 Cardozo L. Rev. de novo 1, 14 (2009) (describing plaintiffs’ expert witnesses in silicone litigation as “charlatans” and the litigation as largely based upon fraud) with Committee on the Safety of Silicone Breast Implants, Institute of Medicine, Safety of Silicone Breast Implants (Wash. D.C. 1999) (reviewing studies, many of which were commissioned by litigation defendants, and which collectively showed lack of association between silicone and autoimmune diseases).

The relation between litigation and research is one that has typically been approached by self-righteous voices, such as David Michaels and David Egilman, and others who have their own deep conflicts of interest.  What is clear is that all litigants, as well as the public, would benefit from enforcing data sharing requirements.  SeeLitigation and Research” (April 15, 2007) (science should not be built upon blind trust of scientists: “Nullius in verba.”).

The Times article emphasized Wicherts’ research about lack of data sharing, and suggested that data sharing could improve the quality of scientific publications.  The time may have come, however, for sterner measures of civil and criminal penalties for scientists who abuse and waste governmental funding, or who aid and abet fraudulent litigation.

New-Age Levellers – Flattening Hierarchy of Evidence

October 30th, 2011

The Levelers were political dissidents in England, in the middle of the 17th century.  Among their causes, Levelers advanced popular sovereignty, equal protection of the law, and religious tolerance.

The political agenda of the Levelers sounds quite noble to 21st century Americans, but their ideals have no place in the world of science:  not all opinions or scientific studies are created equally; not all opinions are worthy of being taken seriously in scientific discourse or in courtroom presentations of science; and not all opinions should be tolerated, especially when they claim causal conclusions based upon shoddy or inadequate evidence.

In some litigations, legal counsel set out to obscure the important quantitative and qualitative distinctions among scientific studies.  Sometimes, lawyers find cooperative expert witnesses, willing to engage in hand waving about “the weight of the evidence,” where the weights are assigned post hoc, in a highly biased fashion.  No study (that favors the claim) left behind.  This is not science, and it is not how science operates, even though some expert witnesses, such as Professor Cranor in the Milward case, have been able to pass off their views as representative of scientific practice.

A sound appreciation of how scientists evaluate studies, and of why not all studies are equal, is essential to any educated evaluation of scientific controversies.  Litigants who face high-quality studies, with results inconsistent with their litigation claims, may well resort to “leveling” of studies.  This leveling may be advanced out of ignorance, but more likely the leveling is an attempt to snooker courts with evidence from exploratory, preliminary, and hypothesis-generating studies as somehow equal to, or greater than, the value of hypothesis-testing studies.

Some of the leveling tactics that have become commonplace in litigation include asserting that:

  • All experts witnesses are the same;
  • All expert witnesses conduct the same analysis;
  • All expert witnesses read articles, interpret them, and offer opinions;
  • All expert witnesses are inherently biased;
  • All expert witnesses select the articles to read and interpret in line with their biases;
  • All epidemiologic studies are the same;
  • All studies are flawed; and
  • All opinions are, in the final analysis, subjective.

This leveling strategy can be seen in Professor Margaret Berger’s introduction to the Reference Manual on Scientific Evidence (RMSE 3d), where she supported an ill-defined “weight-of-the-evidence” approach to causal judgments. SeeLate Professor Berger’s Introduction to the Reference Manual on Scientific Evidence” (Oct. 23, 2011).

Other chapters in the RMSE 3d are at odds with Berger’s introduction.  The epidemiology chapter does not explicitly address the hierarchy of studies, but it does describe cross-sectional, ecological, and secular trend studies are less able to support causal conclusions.  Cross-sectional studies are described as “rarely useful in identifying toxic agents,” RMSE 3d at 556, and as “used infrequently when the exposure of interest is an environmental toxic agent,” RMSE 3d at 561.  Cross-sectional studies are described as hypothesis-generating as opposed to hypothesis testing, although not in those specific terms.  Id. (describing cross-sectional studies as providing valuable leads for future research).  Ecological studies are described as useful for identifying associations, but not helpful in determining whether such associations are causal; and ecological studies are identified as a fertile source of error in the form of the “ecological fallacy.”  Id. at 561 -62.

The epidemiology chapter perhaps weakens its helpful description of the limited role of ecological studies by citing, with apparent approval, a district court that blinked at its gatekeeping responsibility to ensure that testifying expert witnesses did, in fact, rely upon “sufficient facts or data,” as well as upon studies that are “of a type reasonably relied upon by experts in the particular field in forming opinions or inferences upon the subject.” Rule 703. RMSE 3d at 561 n.34 (citing Cook v. Rockwell International Corp., 580 F. Supp. 2d 1071, 1095–96 (D. Colo. 2006), where the district court acknowledged the severe limitations of ecological studies in supporting causal inferences, but opined that the limitations went to the weight of the study). Of course, the insubstantial weight of an ecological study is precisely what may result in the study’s failure to support a causal claim.

The ray of clarity in the epidemiology chapter about the hierarchical nature of studies is muddled by an attempt to level epidemiology and toxicology.  The chapter suggests that there is no hierarchy of disciplines (as opposed to studies within a discipline).  RMSE 3d at 564 & n.48 (citing and quoting symposium paper that “[t]here should be no hierarchy [among different types of scientific methods to determine cancer causation]. Epidemiology, animal, tissue culture and molecular pathology should be seen as integrating evidences in the determination of human carcinogenicity.” Michele Carbone et al., “Modern Criteria to Establish Human Cancer Etiology,” 64 Cancer Res. 5518, 5522 (2004).)  Carbone, of course, is best known for his advocacy of a viral cause (SV40), of human mesothelioma, a claim unsupported, and indeed contradicted, by epidemiologic studies.  His statement does not support the chapter’s leveling of epidemiology and toxicology, and Carbone is, in any event, an unlikely source to cite.

The epidemiology chapter undermines its own description of the role of study design in evaluating causality by pejoratively asserting that most epidemiologic studies are “flawed”:

“It is important to emphasize that all studies have ‘flaws’ in the sense of limitations that add uncertainty about the proper interpretation of the results.9 Some flaws are inevitable given the limits of technology, resources, the ability and willingness of persons to participate in a study, and ethical constraints. In evaluating epidemiologic evidence, the key questions, then, are the extent to which a study’s limitations compromise its findings and permit inferences about causation.”

RSME 3d at 553.  This statement is actually a significant improvement over the second edition, where the authors of the epidemiology chapter asserted, without qualification:

“It is important to emphasize that most studies have flaws.”

RMSE 2d 337.  The “flaws” language from the earlier chapter was used on occasion by courts that were set on ignoring competing interpretations of epidemiologic studies.  Since all or most studies are flawed, why bother figuring out what is valid and reliable?  Just let the jury sort it out.  This is not an aid to gatekeeping, but rather a prescription for allowing the gatekeeper to call in sick.

The current epidemiology chapter essentially backtracks from the harsh connotations of its use of the term “flaws,” by now equating the term with “limitations.”  Flaws and limitations, however, are quite different from one another.  What is left out in the third edition’s description is the sense that there are indeed some studies that are so flawed that they must be disregarded altogether.  There may also be limitations in studies, especially observational studies, which is why the party with the burden of proof should generally not be allowed to proceed with only one or two epidemiologic studies.  Rule 702, after all, requires that an expert opinion to be based upon “sufficient facts or data.”

The RSME 3d chapter on medical evidence is a refreshing break from the leveling approach seen elsewhere.  Here at least, the chapter authors devote several pages to explaining the role of study design in assessing an etiological issue:

3. Hierarchy of medical evidence

With the explosion of available medical evidence, increased emphasis has been placed on assembling, evaluating, and interpreting medical research evidence.  A fundamental principle of evidence-based medicine (see also Section IV.C.5, infra) is that the strength of medical evidence supporting a therapy or strategy is hierarchical.

When ordered from strongest to weakest, systematic review of randomized trials (meta-analysis) is at the top, followed by single randomized trials, systematic reviews of observational studies, single observational studies, physiological studies, and unsystematic clinical observations.150 An analysis of the frequency with which various study designs are cited by others provides empirical evidence supporting the influence of meta-analysis followed by randomized controlled trials in the medical evidence hierarchy.151 Although they are at the bottom of the evidence hierarchy, unsystematic clinical observations or case reports may be the first signals of adverse events or associations that are later confirmed with larger or controlled epidemiological studies (e.g., aplastic anemia caused by chloramphenicol,152 or lung cancer caused by asbestos153). Nonetheless, subsequent studies may not confirm initial reports (e.g., the putative association between coffee consumption and pancreatic cancer).154

John B. Wong, Lawrence O. Gostin, and Oscar A. Cabrera, “Reference Guide on Medical Testimony,” RMSE 3d 687, 723 -24 (2011).  The third edition’s chapter is a significant improvement of the second edition’s chapter on medical testimony, which does not mention the hierarchy of evidence.  Mary Sue Henifin, Howard M. Kipen, and Susan R. Poulter, ” Reference Guide on Medical Testimony,” RMSE 2d 440 (2000).  Indeed, the only time the word “hierarchy” appeared in the entire second edition was in connection with the hierarchy of the federal judiciary.

The tension, contradictions, and differing emphases among the various chapters of the RSME 3d point to an important “flaw” in the new edition.  The chapters appear to have been written largely in isolation, and without much regard for what the other chapters contain.  The chapters overlap, and indeed contradict one another on key points.  Witness Berger’s rejection of the hierarchy of evidence, the epidemiology chapter’s inconstant presentation of the concept without mentioning it by name, and the medical testimony chapter’s embrace and explicit presentation of the hierarchical nature of medical study evidence.  Fortunately, the laissez-faire editorial approach allowed the disagreement to remain, without censoring any position, but the federal judiciary is not aided by the contradiction and tension in the approaches.

Given the importance of the concept, even the medical testimony chapter in RSME 3d may seem to be too little, too late to be helpful to the judiciary.  There are book-length treatments of systematic reviews and “evidence-based medicine”: the three pages in Wong’s chapter barely scratch the surface of this important topic of how evidence is categorized, evaluated, and synthesized in making judgments of causality.

There are many textbooks and articles available to judges and lawyers on how to assess medical studies.  Recently, John Cherrie has posted on his blog, OH-world, about a series of 17 articles, in the journal Aerzteblatt International, on the proper evaluation of medical and epidemiologic studies.

These papers, overall, make the point that not all studies are equal, and that not all evidentiary displays are adequate to support conclusions of causal association.  The papers are available without charge from the journal’s website:

01. Critical Appraisal of Scientific Articles

02. Study Design in Medical Research

03. Types of Study in Medical Research

04. Confidence Interval or P-Value?

05. Requirements and Assessment of Laboratory Tests: Inpatient Admission Screening

06. Systematic Literature Reviews and Meta-Analyses

07. The Specification of Statistical Measures and Their Presentation in Tables and Graphs

08. Avoiding Bias in Observational Studies

09. Interpreting Results in 2×2 Tables

10. Judging a Plethora of p-Values: How to Contend With the Problem of Multiple Testing

11. Data Analysis of Epidemiological Studies

12. Choosing statistical tests

13. Sample size calculation in clinical trials

14. Linear regression analysis

15. Survival analysis

16. Concordance analysis

17. Randomized controlled trials

This year, the Journal of Clinical Epidemiology began publishing a series of papers, known by the acronym GRADE, which aim to provide guidance on how studies are categorized and assessed for their evidential quality in supporting treatments and intervention.  The GRADE project is led by Gordon Guyatt, who is known for having coined the term “evidence-based medicine,” and written widely on the subject.  Guyatt, along with his colleagues including Peter Tugwell (who was one of the court-appointed expert witnesses in MDL 926), has described the GRADE project:

“The ‘Grades of Recommendation, Assessment, Development, and Evaluation’ (GRADE) approach provides guidance for rating quality of evidence and grading strength of recommendations in health care. It has important implications for those summarizing evidence for systematic reviews, health technology assessment, and clinical practice guidelines. GRADE provides a systematic and transparent framework for clarifying questions, determining the outcomes of interest, summarizing the evidence that addresses a question, and moving from the evidence to a recommendation or decision. Wide dissemination and use of the GRADE approach, with endorsement from more than 50 organizations worldwide, many highly influential   http://www.gradeworkinggroup.org/), attests to the importance of this work. This article introduces a 20-part series providing guidance for the use of GRADE methodology that will appear in the Journal of Clinical Epidemiology.”

Gordon Guyatt, Andrew D. Oxman, Holger Schünemann, Peter Tugwell, Andre Knottnerus, “GRADE guidelines – new series of articles in Journal of Clinical Epidemiology,” 64 J. Clin. Epidem. 380 (2011).  See also Gordon Guyatt, Andrew Oxman, et al., for the GRADE Working Group, “Rating quality of evidence and strength of recommendations GRADE: an emerging consensus on rating quality of evidence and strength of recommendations,” 336 Brit. Med. J. 924 (2008).  [pdf]

Of the 20 papers planned, 9 of the GRADE papers have been published to date in the Journal of Clinical Epidemiology:

01 Intro – GRADE evidence profiles & summary of findings tables

02 Framing question & deciding on important outcomes

03 Rating quality of evidence

04 Rating quality of evidence – study limitations (risk of bias)

05 Rating the quality of evidence—publication bias

06 Rating up quality of evidence – imprecision

07 Rating quality of evidence – inconsistency

08 Rating quality of evidence – indirectness

09 Rating up quality of evidence

The GRADE guidance papers focus on the efficacy of treatments and interventions, but in doing so, they evaluate “effects” and are thus applicable to the etiologic issues of alleged harm that find their way into court.  The papers build on other grading systems advanced previously by the Oxford Center for Evidence-Based Medicine, the U.S. Preventive Services Task Force (Agency for Healthcare Research and Quality AHRQ), the Cochrane Collaboration, as well as many individual professional organizations.

GRADE has had some success in harmonizing disparate grading systems, and forging a consensus among organizations that had been using their own systems, such as the  World Health Organization, the American College of Physicians, the American Thoracic Society, the Cochrane Collaboration, the American College of Chest Physicians, the British Medical Journal, and Kaiser Permanente.

There are many other important efforts to provide consensus support for improving the quality of the design, conduct, and reporting of published studies, as well as the interpretation of those studies once published.  Although the RSME 3d does a good job of introducing its readers to the basics of study design, it could have done considerably more to help judges become discerning critics of scientific studies and of conclusions based upon individual or multiple studies.

New Reference Manual on Scientific Evidence Short Shrifts Rule 703

October 16th, 2011

In “RULE OF EVIDENCE 703 — Problem Child of Article VII (Sept. 19, 2011),” I wrote about how Federal Rule of Evidence 703 is generally ignored and misunderstood in current federal practice.  The Supreme Court, in deciding Daubert, shifted the focus to Rule 702, as the primary tool to deploy in admitting, as well as limiting and excluding, expert witness opinion testimony.  The Court’s decision, however, did not erase the need for an additional, independent rule to control the quality of inadmissible materials upon which expert witnesses rely.  Indeed, Rule 702 as amended in 2000, incorporated much of the learning of the Daubert decision, and then some, but it does not address the starting place of any scientific opinion:  the data, the analyses (usually statistical) of data, and the reasonableness of relying upon those data and analyses.  Instead, Rule 702 asks whether the proffered testimony is based upon:

  1. sufficient facts or data,
  2. the product of reliable principles and methods, and
  3. a reliable application of principles and methods to the facts of the case

Noticeably absent from Rule 702, in its current form, is any directive to determine whether the proffered expert witness opinion is based upon facts or data of the sort upon which experts in the pertinent field would reasonably rely.  Furthermore,  Daubert did not address the fulsome importation and disclosure of untrustworthy hearsay opinions through Rule 703.  See Problem Child (discussing the courts’ failure to appreciate the structure of peer-reviewed articles, and the need to ignore the discussion and introduction sections of such articles as often containing speculative opinions and comments).  See also Luciana B. Sollaci & Mauricio G. Pereira, “The introduction, methods, results, and discussion (IMRAD) structure: a fifty-year survey,” 92 J. Med. Libr. Ass’n 364 (2004); Montori, et al., “Users’ guide to detecting misleading claims in clinical research reports,” 329 Br. Med. J. 1093, 1093 (2004) (advising readers on how to avoid being misled by published literature, and counseling readers to “Read only the Methods and Results sections; bypass the Discuss section.”)  (emphasis added).

Given this background, it is disappointing but not surprising that the new Reference Manual on Scientific Evidence severely slights Rule 703.  Using either a word search in the PDF version or the index at end of book tells the story:  There are five references to Rule 703 in the entire RMSE!  The statistics chapter has an appropriate but fleeting reference:

“Or the study might rest on data of the type not reasonably relied on by statisticians or substantive experts and hence run afoul of Federal Rule of Evidence 703. Often, however, the battle over statistical evidence concerns weight or sufficiency rather than admissibility.”

RMSE 3d at 214. At least this chapter acknowledges, however briefly, the potential problem that Rule 703 poses for expert witnesses.  The chapter on survey research similarly discusses how the data collected in a survey may “run afoul” of Rule 703.  RMSE 3d at 361, 363-364.

The chapter on epidemiology takes a different approach by interpreting Rule 703 as a rule of admissibility of evidence:

“An epidemiologic study that is sufficiently rigorous to justify a conclusion that it is scientifically valid should be admissible,184 as it tends to make an issue in dispute more or less likely.185

Id. at 610.  This view is mistaken.  Sufficient rigor in an epidemiologic study is certainly needed for reliance by an expert witness, but such rigor does not make the study itself admissible; the rigor simply permits the expert witness to rely upon a study that is typically several layers of inadmissible hearsay.  See Reference Manual on Scientific Evidence v3.0 – Disregarding Study Validity in Favor of the “Whole Gamish” (Oct. 14, 2011) (discussing the argument put forward by the epidemiology chapter for considering Rule 703 as an exception to the rule against hearsay).

While the treatment of Rule 703 in the epidemiology chapter is troubling, the introductory chapter on the admissibility of expert witness opinion testimony by the late Professor Margaret Berger really sets the tone and approach for the entire volume. See Berger, “The Admissibility of Expert Testimony,” RSME 3d 11 (2011).  Professor Berger never mentions Rule 703 at all!  Gone and forgotten. The omission is not, however, an oversight.  Rule 703, with its requirement of qualifying each study relied upon as having been “reasonably relied upon,” as measured by what experts in the appropriate discipline, is the refutation of Berger’s argument that somehow a pile of weak, flawed studies, taken together can yield a scientifically reliable conclusion. SeeWhole Gamish,” (Oct. 14th, 2011).

Rule 703 is not merely an invitation to trial judges; it is a requirement to look at the discrete studies relied upon to determine whether the building blocks are sound.  Only then can the methods and procedures of science begin to analyze the entire evidentiary display to yield reliable scientific opinions and conclusions.

Reference Manual on Scientific Evidence v3.0 – Disregarding Study Validity in Favor of the “Whole Gamish”

October 14th, 2011

There is much to digest in the new Reference Manual on Scientific Evidence, third edition (RMSE 3d).  Much of what is covered is solid information on the individual scientific and technical disciplines covered.  Although the information is easily available from other sources, there is some value in collecting the material in a single volume for the convenience of judges.  Of course, given that this information is provided to judges from an ostensibly neutral, credible source, lawyers will naturally focus on what is doubtful or controversial in the RMSE.

I have already noted some preliminary concerns, however, with some of the comments in the Preface, by Judge Kessler and Dr. Kassirer.  See “New Reference Manual’s Uneven Treatment of Conflicts of Interest.”  In addition, there is a good deal of overlap among the chapters on statistics, epidemiology, and medical testimony.  This overlap is at first blush troubling because the RMSE has the potential to confuse and obscure issues by having multiple authors address them inconsistently.  This is an area where reviewers should pay close attention.

From first looks at the RMSE 3d, there is a good deal of equivocation between encouraging judges to look at scientific validity, and discouraging them from any meaningful analysis by emphasizing inaccurate proxies for validity, such as conflicts of interest.  (As I have pointed out, the new RSME did not do quite so well in addressing its own conflicts of interest.  SeeToxicology for Judges – The New Reference Manual on Scientific Evidence (2011).”)

The strengths of the chapter on statistical evidence, updated from the second edition, remain, as do some of the strengths and flaws of the chapter on epidemiology.  I hope to write more about each of these important chapters at a later date.

The late Professor Margaret Berger has an updated version of her chapter from the second edition, “The Admissibility of Expert Testimony,” RSME 3d 11 (2011).  Berger’s chapter has a section criticizing “atomization,” a process she describes pejoratively as a “slicing-and-dicing” approach.  Id. at 19.  Drawing on the publications of Daubert-critic Susan Haack, Berger rejects the notion that courts should examine the reliability of each study independently. Id. at 20 & n. 51 (citing Susan Haack, “An Epistemologist in the Bramble-Bush: At the Supreme Court with Mr. Joiner,” 26 J. Health Pol. Pol’y & L. 217–37 (1999).  Berger contends that the “proper” scientific method, as evidenced by works of the International Agency for Research on Cancer, the Institute of Medicine, the National Institute of Health, the National Research Council, and the National Institute for Environmental Health Sciences, “is to consider all the relevant available scientific evidence, taken as a whole, to determine which conclusion or hypothesis regarding a causal claim is best supported by the body of evidence.” Id. at 19-20 & n.52.  This contention, however, is profoundly misleading.  Of course, scientists undertaking a systematic review should identify all the relevant studies, but some of the “relevant” studies may well be insufficiently reliable (because of internal or external validity issues) to answer the research question at hand. All the cited agencies, and other research organizations and researchers, exclude studies that are fundamentally flawed, whether as a result of bias, confounding, erroneous data analyses, or related problems.  Berger cites no support for the remarkable suggestion that scientists do not make “reliability” judgments about available studies when assessing the “totality of the evidence.”

Professor Berger, who had a distinguished career as a law professor and evidence scholar, died in November 2010.  She was no friend of Daubert, but remarkably her antipathy has outlived her.  Her critical discussion of “atomization” cites the notorious decision in Milward v. Acuity Specialty Products Group, Inc., 639 F.3d 11, 26 (1st Cir. 2011), which was decided four months after her passing. Id. at 20 n.51. (The editors note that the published chapter was Berger’s last revision, with “a few edits to respond to suggestions by reviewers.”)

Professor Berger’s contention about the need to avoid assessments of individual studies in favor of the whole gamish must also be rejected because Federal Rule of Evidence 703 requires that each study considered by an expert witness “qualify” for reasonable reliance by virtue of the study’s containing facts or data that are “of a type reasonably relied upon by experts in the particular field forming opinions or inferences upon the subject.”  One of the deeply troubling aspects of the Milward decision is that it reversed the trial court’s sensible decision to exclude a toxicologist, Dr. Martyn Smith, who outran his headlights on issues having to do with a field in which he was clearly inexperienced – epidemiology.

Scientific studies, and especially epidemiologic studies, involve multiple levels of hearsay.  A typical epidemiologic study may contain hearsay leaps from patient to clinician, to laboratory technicians, to specialists interpreting test results, back to the clinician for a diagnosis, to a nosologist for disease coding, to a national or hospital database, to a researcher querying the database, to a statistician analyzing the data, to a manuscript that details data, analyses, and results, to editors and peer reviewers, back to study authors, and on to publication.  Those leaps do not mean that the final results are untrustworthy, only that the study itself is not likely admissible in evidence.

The inadmissibility of scientific studies is not problematic because Rule 703 permits testifying expert witnesses to formulate opinions based upon facts and data, which are not themselves admissible in evidence. The distinction between relied upon, and admissible, studies is codified in the Federal Rules of Evidence, and in virtually every state’s evidence law.

Referring to studies, without qualification, as admissible in themselves is wrong as a matter of evidence law.  The error has the potential to encourage carelessness in gatekeeping expert witnesses’ opinions for their reliance upon inadmissible studies.  The error is doubly wrong if this approach to expert witness gatekeeping is taken as license to permit expert witnesses to rely upon any marginally relevant study of their choosing.  It is therefore disconcerting that the new Reference Manual on Science Evidence (RMSE 3d) fails to make the appropriate distinction between admissibility of studies and admissibility of expert witness opinion that has reasonably relied upon appropriate studies.

Consider the following statement from the chapter on epidemiology:

“An epidemiologic study that is sufficiently rigorous to justify a conclusion that it is scientifically valid should be admissible,184 as it tends to make an issue in dispute more or less likely.185

RMSE 3d at 610.  Curiously, the authors of this chapter have ignored Professor Berger’s caution against slicing and dicing, and speak to a single study’s ability to justify a conclusion. The authors of the epidemiology chapter seem to be stressing that scientifically valid studies should be admissible.  The footnote emphasizes the point:

See DeLuca v. Merrell Dow Pharms., Inc., 911 F.2d 941, 958 (3d Cir. 1990); cf. Kehm v. Procter & Gamble Co., 580 F. Supp. 890, 902 (N.D. Iowa 1982) (“These [epidemiologic] studies were highly probative on the issue of causation—they all concluded that an association between tampon use and menstrually related TSS [toxic shock syndrome] cases exists.”), aff’d, 724 F.2d 613 (8th Cir. 1984). Hearsay concerns may limit the independent admissibility of the study, but the study could be relied on by an expert in forming an opinion and may be admissible pursuant to Fed. R. Evid. 703 as part of the underlying facts or data relied on by the expert. In Ellis v. International Playtex, Inc., 745 F.2d 292, 303 (4th Cir. 1984), the court concluded that certain epidemiologic studies were admissible despite criticism of the methodology used in the studies. The court held that the claims of bias went to the studies’ weight rather than their admissibility. Cf. Christophersen v. Allied-Signal Corp., 939 F.2d 1106, 1109 (5th Cir. 1991) (“As a general rule, questions relating to the bases and sources of an expert’s opinion affect the weight to be assigned that opinion rather than its admissibility. . . .”).”

RMSE 3d at 610 n.184 (emphasis in bold, added).  This statement, that studies relied upon by an expert in forming an opinion may be admissible pursuant to Rule 703, is unsupported by Rule 703 and the overwhelming weight of case law interpreting and applying the rule.  (Interestingly, the authors of this chapter seem to abandon their suggestion that studies relied upon “might qualify for the learned treatise exception to the hearsay rule, Fed. R. Evid. 803(18), or possibly the catchall exceptions, Fed. R. Evid. 803(24) & 804(5),” which was part of their argument in the Second Edition of the RMSE.  RMSE 2d at 335 (2000).)  See also RMSE 3d at 214 (discussing statistical studies as generally “admissible,” but acknowledging that admissibility may be no more than permission to explain the basis for an expert’s opinion).

The cases cited by the epidemiology chapter, Kehm and Ellis, both involved “factual findings” in public investigative or evaluative reports, which were independently admissible under Federal Rule of Evidence 803(8)(C).  See Ellis, 745 F.2d at 299-303; Kehm, 724 F.2d at 617-18.  As such, the cases hardly support the chapter’s suggestion that Rule 703 is a rule of admissibility for epidemiologic studies.

Here the RMSE, in one sentence, confuses Rule 703 with an exception to the rule against hearsay, which would prevent the statistical studies from being received in evidence.  The point is reasonably clear, however, that the studies “may be offered” to explain an expert witness’s opinion.  Under Rule 705, that offer may also be refused. The offer, however, is to “explain,” not to have the studies admitted in evidence.

The RMSE is certainly not alone in advancing this notion that studies are themselves admissible.  Other well-respected evidence scholars lapse into this position:

“Well conducted studies are uniformly admitted.”

David L. Faigman, et al., Modern Scientific Evidence:  The Law and Science of Expert Testimony v.1, § 23:1,at 206 (2009)

Evidence scholars should not conflate admissibility of the epidemiologic (or other) studies with the ability of an expert witness to advert to a study to explain his or her opinion.  The testifying expert witness really has no need to become a conduit for off-hand comments and opinions in the introduction or discussion section of relied upon articles, and the wholesale admission of such hearsay opinions undermines the court’s control over opinion evidence.  Rule 703 authorizes reasonable reliance upon “facts and data,” not every opinion that creeps into the published literature.

New Reference Manual’s Uneven Treatment of Conflicts of Interest

October 12th, 2011

The new, third edition of the Reference Manual on Scientific Evidence (RMSE) appears to get off to a good start in the Preface by Judge Kessler and Dr. Kassirer, when they note that the Supreme Court mandated federal courts to

“examine the scientific basis of expert testimony to ensure that it meets the same rigorous standard employed by scientific researchers and practitioners outside the courtroom.”

RMSE at xiii.  The preface falters, however, on two key issues, causation and conflicts of interest, which are taken up as an introduction to the new volume.

1. CAUSATION

The authors tell us in squishy terms that causal assessments are judgments:

“Fundamentally, the task is an inferential process of weighing evidence and using judgment to conclude whether or not an effect is the result of some stimulus. Judgment is required even when using sophisticated statistical methods. Such methods can provide powerful evidence of associations between variables, but they cannot prove that a causal relationship exists. Theories of causation (evolution, for example) lose their designation as theories only if the scientific community has rejected alternative theories and accepted the causal relationship as fact. Elements that are often considered in helping to establish a causal relationship include predisposing factors, proximity of a stimulus to its putative outcome, the strength of the stimulus, and the strength of the events in a causal chain.”

RMSE at xiv.

The authors leave the inferential process as a matter of “weighing evidence,” but without saying anything about how the scientific community does its “weighing.”  Language about “proving” causation is also unclear because “proof” in scientific parlance connotes a demonstration, which we typically find in logic or in mathematics.  Proving empirical propositions suggests a bar set too high such that the courts must inevitable lower the bar considerably.  The question is, of course, how low will judges go to admit evidence.

The authors thus introduce hand waving and excuses for why evidence can be weighed differently in court proceedings from the world of science:

“Unfortunately, judges may be in a less favorable position than scientists to make causal assessments. Scientists may delay their decision while they or others gather more data. Judges, on the other hand, must rule on causation based on existing information. Concepts of causation familiar to scientists (no matter what stripe) may not resonate with judges who are asked to rule on general causation (i.e., is a particular stimulus known to produce a particular reaction) or specific causation (i.e., did a particular stimulus cause a particular consequence in a specific instance). In the final analysis, a judge does not have the option of suspending judgment until more information is available, but must decide after considering the best available science.”

RMSE at xiv.  But the “best available science” may be pretty crummy, and the temptation to turn desperation into evidence (“well, it’s the best we have now”) is often severe.  The authors of the Preface signal that “inconclusive” is not a judgment open to judges charged with expert witness gatekeeping.  If the authors truly mean to suggest that judges should go with whatever is dished out as “the best available science,” then they have overlooked the obvious:  Rule 702 opens the door to “scientific, technical, or other specialized knowledge,” not to hunches, suggestive but inconclusive evidence, and wishful thinking about how the science may turn out when further along.  Courts have a choice to exclude expert witness opinion testimony that is based upon incomplete or inconclusive evidence.

2. CONFLICTS OF INTEREST

Surprisingly, given the scope of the scientific areas covered in the RMSE, the authors discuss conflicts of interest (COI) at some length.  Conflicts of interest are a fact of life in all endeavors, and it is understandable counsel judges and juries to try to identify, assess, and control them.  COIs, however, are weak proxies for unreliability.  The emphasis given here is undue because federal judges are misled into thinking that they can discern unreliability from COI, when they should be focused on the data and the analysis.

The authors of the Preface set about to use COI as a basis for giving litigation plaintiffs a pass, and for holding back studies sponsored by corporate defendants.

“Conflict of interest manifests as bias, and given the high stakes and adversarial nature of many courtroom proceedings, bias can have a major influence on evidence, testimony, and decisionmaking. Conflicts of interest take many forms and can be based on religious, social, political, or other personal convictions. The biases that these convictions can induce may range from serious to extreme, but these intrinsic influences and the biases they can induce are difficult to identify. Even individuals with such prejudices may not appreciate that they have them, nor may they realize that their interpretations of scientific issues may be biased by them. Because of these limitations, we consider here only financial conflicts of interest; such conflicts are discoverable. Nonetheless, even though financial conflicts can be identified, having such a conflict, even one involving huge sums of money, does not necessarily mean that a given individual will be biased. Having a financial relationship with a commercial entity produces a conflict of interest, but it does not inevitably evoke bias. In science, financial conflict of interest is often accompanied by disclosure of the relationship, leaving to the public the decision whether the interpretation might be tainted. Needless to say, such an assessment may be difficult. The problem is compounded in scientific publications by obscure ways in which the conflicts are reported and by a lack of disclosure of dollar amounts.

Judges and juries, however, must consider financial conflicts of interest when assessing scientific testimony. The threshold for pursuing the possibility of bias must be low. In some instances, judges have been frustrated in identifying expert witnesses who are free of conflict of interest because entire fields of science seem to be co-opted by payments from industry. Judges must also be aware that the research methods of studies funded specifically for purposes of litigation could favor one of the parties. Though awareness of such financial conflicts in itself is not necessarily predictive of bias, such information should be sought and evaluated as part of the deliberations.”

RMSE at xiv-xv.  All in all, rather misleading advice.  Financial conflicts are not the only conflicts that can be “discovered.”  Often expert witnesses will have political and organizational alignments, which will show deep-seated ideological alignments with the party for which they are testifying.  For instance, in one silicosis case, an expert witness in the field of history of medicine testified, at an examination before trial, that his father suffered from a silica-related disease.  This witness’s alignment with Marxist historians and his identification with radical labor movements made his non-financial conflicts obvious, although these COI would not necessarily have been apparent from his scholarly publications alone.

How low will the bar be set for discovering COI?  If testifying expert witnesses are relying upon textbooks, articles, essays, will federal courts open the authors/hearsay declarants up to searching discovery of their finances?

Also misleading is the suggestion that “entire fields of science seem to be co-opted by payments from industry.”  Do the authors mean to exclude the plaintiffs’ lawyer litigation industry, which has grown so large and politically powerful in this country?  In litigations in which I have been involved, I have certainly seen plaintiffs’ counsel, or their proxies – labor unions or “victim support groups” provide substantial funding for studies.  The Preface authors themselves show an untoward bias by their pointing out industry payments without giving balanced attention to other interested parties’ funding of scientific studies.

The attention to COI is also surprising given that one of the key chapters, for toxic tort practitioners, was written by Dr. Bernard D. Goldstein, who has testified in toxic tort cases, mostly (but not exclusively) for plaintiffs.  See, e.g., Parker v. Mobil Oil Corp., 7 N.Y.3d 434, 857 N.E.2d 1114, 824 N.Y.S.2d 584 (2006); Exxon Corp. v. Makofski, 116 SW 3d 176 (Tex. Ct. App. 2003).  The Makofsky case is particularly interesting because Dr. Goldstein was forced to explain why he was willing to opine that benzene caused acute lymphocytic leukemia, despite the plethora of published studies finding no statistically significant relationship.  Dr. Goldstein resorted to the inaccurate notion that scientific “proof” of causation requires 95 percent certainty, whereas he imposed only a 51 percent certainty for his medico-legal testimonial adventures. Dr. Goldstein also attempted to justify the discrepancy from the published literature by adverting to the lower standards used by federal regulatory agencies and treating physicians. Id.

These explanations are particularly concerning because they reflect basic errors in statistics and in causal reasoning.  The 95 percent derives from the use of the same percentage in confidence intervals, but the probability involved there is not the probability of the association’s being correct, and it has nothing to do with the probability in the belief that an association is real or is causal.  (Thankfully the RMSE chapter on statistics gets this right, but my fear is that judges will skip over the more demanding chapter on statistics and place undue weight on the toxicology chapter, written by Dr. Goldstein.)  The reference to federal agencies (OSHA, EPA, etc.) and to treating physicians was meant, no doubt, to invoke precautionary principle concepts as a justification for some vague, ill-defined, lower standard of causal assessment.

The Preface authors might well have taken their own counsel and conducted a more searching assessment of COI among authors of Reference Manual.  Better yet, the authors might have focused the judiciary on the data and the analysis.

Toxicology for Judges – The New Reference Manual on Scientific Evidence (2011)

October 5th, 2011

I have begun to dip into the massive third edition of the Reference Manual on Scientific Evidence.  To date, there have been only a couple of acknowledgments of this new work, which was released to the public on September 28, 2011.  SeeA New Day – A New Edition of the Reference Manual of Scientific Evidence”; and David Kaye, “Prometheus Unbound: Releasing the New Edition of the FJC Reference Manual on Scientific Evidence.”

Like previous editions, the substantive scientific areas are covered in discrete chapters, written by subject matter specialists, often along with a lawyer who addresses the legal implications and judicial treatment of that subject matter.  From my perspective, the chapters on statistics, epidemiology, and toxicology are the most important in my practice and in teaching, and I decided to start with the toxicology.  The toxicology chapter, “Reference Guide on Toxicology,” in the third edition is written by Professor Bernard D. Goldstein, of the University of Pittsburgh Graduate School of Public Health, and Mary Sue Henifin, a partner in the law firm of Buchanan Ingersoll, P.C.

CONFLICTS OF INTEREST

At the question and answer session of the public release ceremony, one gentleman rose to note that some of the authors were lawyers with big firm affiliations, which he supposed must mean that they represent mostly defendants.  Based upon his premise, he asked what the review committee had done to ensure that conflicts of interest did not skew or distort the discussions in the affected chapters.  Dr. Kassirer and Judge Kessler responded by pointing out that the chapters were peer reviewed by outside reviewers, and reviewed by members of the supervising review committee.  The questioner seemed reassured, but now that I have looked at the toxicology chapter, I am not so sure.

The questioner’s premise that a member of a large firm will represent mostly defendants and thus have a pro-defense  bias is probably a common perception among unsophisticated lay observers.  What is missing from their analysis is the realization that although gatekeeping helps the defense lawyers’ clients, it takes away legal work from firms that represent defendants in the litigations that are pretermitted by effective judicial gatekeeping.  Erosion of gatekeeping concepts, however, inures to the benefit of plaintiffs, their counsel, as well as the expert witnesses engaged on behalf of plaintiffs in litigation.

The questioner’s supposition in the case of the toxicology chapter, however, is doubly flawed.  If he had known more about the authors, he would probably not have asked his question.  First, the lawyer author, Ms. Henifin, is known for having taken virulently anti-manufacturer positions.  See Richard M. Lynch and Mary S. Henifin, “Causation in Occupational Disease: Balancing Epidemiology, Law and Manufacturer Conduct,” 9 Risk: Health, Safety & Environment 259, 269 (1998) (conflating distinct causal and liability concepts, and arguing that legal and scientific causal criteria should be abrogated when manufacturing defendant has breached a duty of care).

As for the scientist author of the toxicology chapter, Professor Goldstein, the casual reader of the chapter may want to know that he has testified in any number of toxic tort cases, almost invariably on the plaintiffs’ side.  Unlike the defense lawyer, who loses business revenue, when courts shut down unreliable claims, plaintiffs’ testifying or consulting expert witnesses stand to gain by minimalist expert witness opinion gatekeeping.  Given the economic asymmetries, the reader must thus want to know that Prof. Goldstein was excluded as an expert witness in some high-profile toxic tort cases.  See, e.g., Parker v. Mobil Oil Corp., 7 N.Y.3d 434, 857 N.E.2d 1114, 824 N.Y.S.2d 584 (2006) (dismissing leukemia (AML) claim based upon claimed low-level benzene exposure from gasoline) , aff’g 16 A.D.3d 648 (App. Div. 2d Dep’t 2005).  No; you will not find the Parker case cited in the Manual‘s chapter on toxicology. (Parker is, however, cited in the chapter on exposure science.)

I have searched but I could not find any disclosure of Professor Goldstein’s conflicts of interests in this new edition of the Reference Manual.  I would welcome a correction if I am wrong.  Having pointed out this conflict, I would note that financial conflicts of interest are nothing really compared to ideological conflicts of interest, which often propel scientists into service as expert witnesses.

HORMESIS

One way that ideological conflicts might be revealed is to look for imbalances in the presentation of toxicologic concepts.  Most lawyers who litigate cases that involve exposure-response issues are familiar with the “linear no threshold” (LNT) concept that is used frequently in regulatory risk assessments, and which has metastasized to toxic tort litigation, where LNT often has no proper place.

LNT is a dubious assumption because it claims to “known” the dose response at very low exposure levels in the absence of data.  There is a thin plausibility for genotoxic chemicals claimed to be carcinogens, but even that plausibility evaporates when one realizes that there are defense and repair mechanisms to genotoxicity, which must first be saturated before there can be a carcinogenic response.  Hormesis is today an accepted concept that describes a dose-response relationship that shows a benefit at low doses, but harm at high doses.

The toxicology chapter in the Reference Manual has several references to LNT but none to hormesis.  That font of all knowledge, Wikipedia reports that hormesis is controversial, but so is LNT.  This is the sort of imbalance that may well reflect an ideological bias.

One of the leading textbooks on toxicology describes hormesis:

“There is considerable evidence to suggest that some non-nutritional toxic substances may also impart beneficial or stimulatory effects at low doses but that, at higher doses, they produce adverse effects. This concept of “hormesis” was first described for radiation effects but may also pertain to most chemical responses.”

Curtis D. Klaassen, Casarett & Doull’s Toxicology: The Basic Science of Poisons 23 (7th ed. 2008) (internal citations omitted).

Similarly, the Encyclopedia of Toxicology describes hormesis as an important phenomenon in toxicologic science:

“This type of dose–response relationship is observed in a phenomenon known as hormesis, with one explanation being that exposure to small amounts of a material can actually confer resistance to the agent before frank toxicity begins to appear following exposures to larger amounts.  However, analysis of the available mechanistic studies indicates that there is no single hormetic mechanism. In fact, there are numerous ways for biological systems to show hormetic-like biphasic dose–response relationship. Hormetic dose–response has emerged in recent years as a dose–response phenomenon of great interest in toxicology and risk assessment.”

Philip Wexler, Bethesda, et al., eds., 2 Encyclopedia of Toxicology 96 (2005).  One might think that hormesis would also be of great interest to federal judges, but they will not learn about it from reading the Reference Manual.

Hormesis research has come into its own.  The International Dose-Response Society, which “focus[es] on the dose-response in the low-dose zone,” publishes a journal, Dose-Response, and a newsletter, BELLE:  Biological Effects of Low Level Exposure.  In 2009, two leading researchers in the area of hormesis published a collection of important papers:  Mark P. Mattson and Edward J. Calabrese, eds., Hormesis: A Revolution in Biology, Toxicology and Medicine (N.Y. 2009).

A check in PubMed shows that LNT has more “hits” than “hormesis” or “hermetic,” but still the latter phrases exceed 1,267 references, hardly insubstantial.  In actuality, there are many more hermetic relationships identified in the scientific literature, which often fails to identify the relationship by the term hormesis or hermetic.  See Edward J. Calabrese and Robyn B. Blain, “The hormesis database: The occurrence of hormetic dose responses in the toxicological literature,” 61 Regulatory Toxicology and Pharmacology 73 (2011) (reviewing about 9,000 dose-response relationships for hormesis, to create a database of various aspects of hormesis).  See also Edward J. Calabrese and Robyn B. Blain, “The occurrence of hormetic dose responses in the toxicological literature, the hormesis database: An overview,” 202 Toxicol. & Applied Pharmacol. 289 (2005) (earlier effort to establish hormesis database).

The Reference Manual’s omission of hormesis is regrettable.  Its inclusion of references to LNT but not to hormesis appears to result from an ideological bias.

QUESTIONABLE SUBSTANTIVE OPINIONS

One would hope that the toxicology chapter would not put forward partisan substantive positions on issues that are currently the subject of active litigation.  Fondly we would hope that any substantive position advanced would at least be well documented.

For at least one issue, the toxicology chapter dashes our fondest hopes.  Table 1 in the chapter presents a “Sample of Selected Toxicological End Points and Examples of Agents of Concern in Humans.” No documentation or citations are provided for this table.  Most of the exposure agent/disease outcome relationships in the table are well accepted, but curiously at least one agent-disease pair is the subject of current litigation is wildly off the mark:

Parkinson’s disease and manganese

Reference Manual at 653.  If the chapter’s authors had looked, they would have found that Parkinson’s disease is almost universally accepted to have no known cause, except among a few plaintiffs’ litigation expert witnesses.  They would also have found that the issue has been addressed carefully and the claimed relationship or “concern” has been rejected by the leading researchers in the field (who have no litigation ties).  See, e.g., Karin Wirdefeldt, Hans-Olaf Adami, Philip Cole, Dimitrios Trichopoulos, and Jack Mandel, “Epidemiology and etiology of Parkinson’s disease: a review of the evidence.  26 European J. Epidemiol. S1, S20-21 (2011); Tomas R. Guilarte, “Manganese and Parkinson’s Disease: A Critical Review and New Findings,” 118 Environ Health Perspect. 1071, 1078 (2010) (“The available evidence from human and non­human primate studies using behavioral, neuroimaging, neurochemical, and neuropathological end points provides strong sup­port to the hypothesis that, although excess levels of [manganese] accumulation in the brain results in an atypical form of parkinsonism, this clini­cal outcome is not associated with the degen­eration of nigrostriatal dopaminergic neurons as is the case in PD.”)

WHEN ALL YOU HAVE IS A HAMMER, EVERYTHING LOOKS LIKE A NAIL

The substantive specialist author, Professor Goldstein, is not a physician; nor is he an epidemiologist.  His professional focus on animal and cell research shows, and biases the opinions offered in this chapter.

“In qualitative extrapolation, one can usually rely on the fact that a compound causing an effect in one mammalian species will cause it in another species. This is a basic principle of toxicology and pharmacology.  If a heavy metal, such as mercury, causes kidney toxicity in laboratory animals, it is highly likely to do so at some dose in humans.”

Reference Manual at 646.

Such extrapolations may make sense in regulatory contexts, where precauationary judgments are of interest, but they hardly can be said to be generally accepted in controversies in civil actions over actual causation.  Crystalline silica, for instance, causes something resembling lung cancer in rats, but not in mice, guinea pigs, or hamsters.  It hardly makes sense to ask juries to decide whether the plaintiff is more like a rat than a mouse.

For a sober second opinion to the toxicology chapter, one may consider the views of some well-known authors:

“Whereas the concordance was high between cancer-causing agents initially discovered in humans and positive results in animal studies (Tomatis et al., 1989; Wilbourn et al., 1984), the same could not be said for the reverse relationship: carcinogenic effects in animals frequently lacked concordance with overall patterns in human cancer incidence (Pastoor and Stevens, 2005).”

Hans-Olov Adami, Sir Colin L. Berry, Charles B. Breckenridge, Lewis L. Smith, James A. Swenberg, Dimitrios Trichopoulos, Noel S. Weiss, and Timothy P. Pastoor, “Toxicology and Epidemiology: Improving the Science with a Framework for Combining Toxicological and Epidemiological Evidence to Establish Causal Inference,” 122 Toxciological Sciences 223, 224 (2011).

Once again, there is a sense that the scholarship of the toxicology chapter is not as complete or thorough as we would hope.

Diluting “Reasonable Degree of Medical Certainty” – An AAJ-Restatement “Tool” to Help Plaintiffs

October 3rd, 2011

In “the Top Reason that the ALI’s Restatement of Torts Should Steer Clear of Partisan Conflicts,” I pointed out the inappropriateness of advertising the ALI’s Restatement of Torts to the organized plaintiffs’ bar, much as the plaintiffs’ bar advertises potential huge recoveries for the latest tort du jour.  See Michael D. Green & Larry S. Stewart, “The New Restatement’s Top 10 Tort Tools,” Trial 44 (April 2010).

Some of the authors’ tort tool kit may be unexceptionable.  Among these authors’ top ten tort tools, however, is the new Restatement’s edict that “reasonable degree of medical certainty” means, or should mean, nothing more than saying “more likely than not.”  The authors criticize the reasonable certainty standard with an abbreviated rendition of the Restatement’s critique:

“Many courts hold that expert opinion must be expressed in terms of medical or scientific certainty’. Requiring certainty seems to impose a criminal law-like burden of proof that is inconsistent with civil burdens of preponderance of the evidence to establish a fact. Such a requirement is also problematic at best because medical and scientific communities have no such ‘reasonable certainty’ standard. The standard then becomes whatever the attorney who hired the expert tells the expert it means or, absent that, whatever the expert imagines it means. Section 28, comment e, of the Restatement criticizes this standard and makes clear that the same preponderance standard (or ‘more likely than not’ standard), which is universally applied in all aspects of civil cases, also applies to expert testimony.”

Id. at 46-47.

Well, the more likely than not standard is not “universally applied in all aspects of civil cases,” because several states require exemplary damages to be proven by “clear and convincing” or greater evidence.  In some states, the burden of proof in fraud cases is higher than a mere preponderance of the evidence. This premise of the authors’ article is incorrect.

But even if the authors were correct that the preponderance standard applied “in all aspects” of civil cases, their scholarship would remain suspect, as others and I have previously pointed out.  SeeReasonable Degree of Medical Certainty,” and “More Uncertainty About Reasonable Degree of Medical Certainty.”

1. The Restatement’s Treatment of Expert Witness Evidentiary Rules Exceeded the Scope of the Tort Restatement.

The most peculiar aspect of this “top tool,” is that it has nothing to do with the law of torts.  The level of certitude required of an expert witness is an evidentiary and a procedural issue. Of course the issue comes up in tort cases, which frequently involve medical and scientific causation opinions, as well as other expert witness opinions.  The issue, however, comes up in all cases that involve expert witnesses:  trust and estates, regulatory, environmental, securities fraud, commercial, and other cases.

The Restatement of Torts weakly acknowledges its frolic and detour in treating a procedural issue concerning the admissibility of expert witness opinion testimony, by noting that it does “not address any other requirements for the admissibility of an expert witness’s testimony, including qualifications, expertise, investigation, methodology, or reasoning.” Restatement (Third) of Torts: Liability for Physical and Emotional Harm § 28, cmt. e (2010).  The certitude issue has nothing special to do with the substantive law of torts, and should not have been addressed in the torts restatement.

2. The Restatement’s Treatment of “Reasonable Degree of Medical Certainty” Has No Relevance to the Burden of Proof in Tort Cases.

The expert witness certitude issue has nothing to do with the burden of proof, and the Restatement should not have confused and conflated the burden of proof with the standard of certitude for expert witnesses.  The clear but unacceptable implication is that expert witnesses in criminal cases must testify to certitude “beyond a reasonable doubt,” and in claims for equitable relief, expert witnesses may share only opinions that are made, in their minds, by “clear and convincing evidence.”  There is no support in law or logic for the identification of witness certitude with parties’ burdens of proof.

Comment e states the critique more fully:

“If courts do interpret the reasonable-certainty standard to require a level of certitude greater than the preponderance-of-the-evidence standard requires, this creates a troubling inconsistency between standards for the admissibility of evidence and the threshold required for sufficiency of proof. The threshold for admissibility should not be higher than the threshold to sufficiency.  Moreover, the reasonable-certainty standard provides no assurance of the quality of the expert’s qualifications, expertise, investigation, methodology, or reasoning.  Thus, the Section adopts the same preponderance standard that is universally adopted in civil cases.  Direct and cross-examination can be employed to flesh out the degree of certainty with which an expert’s opinion is held and to identify opinions that are speculative and therefore inadmissible.”

Id. The critique badly misfires because there is no inconsistency and no trouble in having different standards for the admissibility of opinion evidence and the burden of proof.  As noted, expert witnesses testify on causation and other issues in criminal, equity, and tort cases, all with different burdens of proof.  Juries in criminal and tort cases must apply instructions on burdens of proof to an entire evidentiary display, not just the expert witnesses’ opinions.  In logic and law, there ultimately must be different burdens for admissibility of expert witness testimony and for sufficiency of a party’s proofs.

3. The Restatement’s Treatment of “Reasonable Degree of Medical Certainty” Incoherently Confuses Two Different Standards.

We can see that Comment e’s approach to legislating an equivalence between expert witness certitude and the burden must fail even on its own terms.  Consider the legal consequences of tort claimants, with the burden of proof, who produce expert witnesses to opine about key elements (e.g., causation) of torts by stating that their opinions were held by a mere “preponderance of the evidence.”

If this probability is understood to be only infinitesimally greater than 50%, then courts would have to direct verdicts in many (and perhaps most) cases.

Courts must ensure that a rational jury can find for the party with the burden of proof.  Juries must evaluate the credibility and reliability of expert witnesses, their opinions, as well as the predicate facts for those opinions.  If those expert witness opinions were barely greater than 50% probable on an essential element, then unless the witnesses had perfect credibility, and all predicate facts were as probable as claimed by the witnesses, then juries would frequently have to reject the witnesses’ opinions.  The bare preponderance of the expert witnesses’ opinions would result in an overall probability of the essential element less than 50%.

4. The Restatement Incorrectly Implies that Expert Witnesses Can Quantify Their Opinions in Probabilistic Terms.

There are even more far-reaching problems with simply substituting “more likely than not” for RDMC as a threshold requirement of expert witness testimony.  Comment e implies that expert witnesses can discern the difference between an opinion that they believe is “more likely than not” and another which is “as likely as not.” On some occasions, there may be opinions that derive from quantitative reasoning, for which an expert witness could truly say, with some level of certainty, that his or her opinion is “more likely than not.” On most occasions, an expert witness’s degree of certainty is a qualitative opinion that simply does not admit of a quantitative characterization. The Restatement’s comment perpetuates this confusion by casting the reasonable certainty standard as a bare probability.

Comment e further suggests that expert witnesses are themselves expert in assessing their own level of certainty, and that they have the training and experience to distinguish an opinion that is 50.1% likely from another that is only 50% likely. The assignment of precise mathematical probabilities to personal, subjective beliefs is a doubtful exercise, at best. See, e.g., Daniel Kahneman and Amos Tversky, “Judgment under Uncertainty: Heuristics and Biases,” 185 Science 1124 (1974).

5. The Restatement Incorrectly Labels “Reasonable Degree of Medical Certainty” As An Empty Formalism.

Comment e ignores the epistemic content of reasonable certainty, which bears an uncanny resemblance to the knowledge requirement of Rule 702.  The “mantra” is helpful to the extent it imposes an objective epistemic standard, especially in states that have failed to impose, or that have abrogated, expert witness gatekeeping.  In some states, there is no meaningful expert witness gatekeeping under either the Frye standard or Rule 702. See, e.g., “Expert Evidence Free-for-All in Washington State.”  See also Joseph Sanders, “Science, Law, and the Expert Witness,” 72 Law & Contemporary Problems 63, 87 & n. 118 (2009) (noting that the meaning of “reasonable degree of scientific certainty” is unclear, but that it can be understood as an alternative formulation of Kumho’s “same intellectual rigor” test).

Some of these “top” tools may be defective.  The authors may need good defense counsel.