“Then time will tell just who fell
And who’s been left behind”

                  Dylan, “Most Likely You Go Your Way” (1966)

 

When the Daubert case headed to the Supreme Court, it had 22 amicus briefs in tow. Today that number is routine for an appeal to the high court, but in 1992, it was a signal of intense interest in the case among both the scientific and legal community. To the litigation industry, the prospect of judicial gatekeeping of expert witness testimony was an anathema. To the manufacturing industry, the prospect was precious to defend against specious claiming.

With the benefit of 25 years of hindsight, a look at some of those amicus briefs reveals a good deal about the scientific and legal acumen of the “friends of the court.” Not all amicus briefs in the case were equal; not all have held up well in the face of time. The amicus brief of the American Association for the Advancement of Science and the National Academy of Science was a good example of advocacy for the full implementation of gatekeeping on scientific principles of valid inference.¹ Other amici urged an anything goes approach to judicial oversight of expert witnesses.

One amicus brief often praised by Plaintiffs’ counsel was submitted by Professor Kenneth Rothman and colleagues.² This amicus brief is still cited by parties who find support in the brief for their excuses for not having consistent, valid, strong, and statistically significance evidence to support their claims of causation. To be sure, Rothman did target statistical significance as a strict criterion of causal inference, but there is little support in the brief for the loosey-goosey style of causal claiming that is so prevalent among lawyers for the litigation industry. Unlike the brief filed by the AAAS and the National Academy of Science, Rothman’s brief abstained from the social policies implied by judicial gatekeeping or its rejection. Instead, Rothman’s brief wet out to make three narrow points:

(1) courts should not rely upon strict statistical significance testing for admissibility determinations;

(2) peer review is not an appropriate touchstone for the validity of an expert witness’s opinion; and

(3) unpublished, non-peer-reviewed “reanalysis” of studies is a routine part of the scientific process, and regularly practiced by epidemiologists and other scientists.

Rothman was encouraged to target these three issues by the lower courts’ opinions in the Daubert case, in which the courts made blanket statements about the role of absent statistical significance and peer review, and the illegitimacy of “re-analyses” of published studies.

Professor Rothman has made many admirable contributions to epidemiologic practice, but the amicus brief submitted by him and his colleagues falls into the trap of making the sort of blanket general statements that they condemned in the lower courts’ opinions. Of the brief’s three points, the first, about statistical significance is the most important for epidemiologic and legal practice. Despite reports of an odd journal here or there “abolishing” p-values, most medical journals continue to require the presentation of either p-values or confidence intervals. In the majority of medical journals, 95% confidence intervals that exclude a null hypothesis risk ratio of 1.0, or risk difference of 0, are labelled “statistically significant,” sometimes improvidently in the presence of multiple comparisons and lack of pre-specification of outcome.

For over three decades, Rothman has criticized the prevailing practice on statistical significance. Professor Rothman is also well known for his advocacy for the superiority of confidence intervals over p-values in conveying important information about what range of values are reasonably compatible with the observed data.³ His criticisms of p-values and his advocacy for estimation with intervals have pushed biomedical publishing to embrace confidence intervals as more informative than just p-values. Still, his views on statistical significance have never gained complete acceptance at most clinical journals. Biomedical scientists continue to interpret 95% confidence intervals, at least in part, as to whether they show “significance” by excluding the null hypothesis value of no risk difference or of risk ratios equal to 1.0.

The first point in Rothman’s amicus brief is styled:

“THE LOWER COURTS’ FOCUS ON SIGNIFICANCE TESTING IS BASED ON THE INACCURATE ASSUMPTION THAT ‘STATISTICAL SIGNIFICANCE’ IS REQUIRED IN ORDER TO DRAW INFERENCES FROM EPIDEMIOLOGICAL INFORMATION”

The challenge by Rothman and colleagues to the “assumption” that statistical significance is necessary is what, of course, has endeared this brief to the litigation industry. A close read of the brief, however, shows that Rothman’s critique of the assumption is equivocal. Rothman et amici characterized the lower courts as having given:

“blind deference to inappropriate and arcane publication standards and ‘significance testing’.”⁴

The brief is silent about what might be knowing deference, or appropriate publication standards. To be sure, judges have often poorly expressed their reasoning for deciding scientific evidentiary issues, and perhaps poor communication or laziness by judges was responsible for Rothman’s interest in joining the Daubert fray. Putting aside the unclear, rhetorical, and somewhat hyperbolic use of “arcane” in the quote above, the suggestion of inappropriate blind deference is itself expressed in equivocal terms in the brief. At times the authors rail at the use of statistical significance as the “sole” criterion, and at times, they seem to criticize its use at all.

At least twice in their brief, Rothman and friends declare that the lower court:

“misconstrues the validity and appropriateness of significance testing as a decision making tool, apparently deeming it the sole test of epidemiological hypotheses.”⁵

* * * * * *

“this Court should reject significance testing as the sole acceptable criterion of scientific validity in epidemiology.”⁶

Characterizing “statistical significance” as not the sole test or criterion of scientific inference is hardly controversial, and it implies that statistical significance is one test, criterion, or factor among others. This position is consistent with the current ASA Statement on Significance Testing.⁷ There is, of course, much more to evaluate in a study or a body of studies, than simply whether they individually or collectively help us to exclude chance as an explanation for their findings.

Statistical Significance Is Not Necessary At All

Elsewhere, Rothman and friends take their challenge to statistical significance testing beyond merely suggesting that such testing is only one test or criterion among others. Indeed, their brief in other places states their opinion that significance testing is not necessary at all:

“Testing for significance, however, is often mistaken for a sine qua non of scientific inference.”⁸

And at other times, Rothman and friends go further yet and claim not only that significance is not necessary, but that it is not even appropriate or useful:

“Significance testing, however, is neither necessary nor appropriate as a requirement for drawing inferences from epidemiologic data.”⁹

Rothman compares statistical significance testing with “scientific inference,” which is not a mechanical, mathematical procedure, but rather a “thoughtful evaluation[] of possible explanations for what is being observed.”¹⁰ Significance testing, in contrast,” is “merely a statistical tool,” used inappropriately “in the process of developing inferences.”¹¹ Rothman suggests that the term “statistical significance” could be eliminated from scientific discussions without loss of meaning, and this linguistic legerdemain shows that the phrase is unimportant in science and in law.¹² Rothman’s suggestion, however, ignores that causal assessments have always required an evaluation of the play of chance, especially for putative causes, which are neither necessary nor sufficient, and which modify underlying stochastic processes by increasing or decreasing the probability of a specified outcome. Asserting that statistical significance is misleading because it never describes the size of an association, which the Rothman brief does, is like telling us that color terms tell us nothing about the mass of a body.

The Rothman brief does make the salutary point that labeling a study outcome as not “statistically significant” carries the danger that the study’s data have no value, or that the study may be taken to reject the hypothesized association. In 1992, such an interpretation may have been more common, but today, in the face of the proliferation of meta-analyses, the risk of such interpretations of single study outcomes is remote.

Questionable History of Statistics

Rothman suggests that the development of statistical hypothesis testing occurred in the context of agricultural and quality-control experiments, which required yes-no answers for future action.¹³ This suggestion clearly points at Sir Ronald Fisher and Jerzy Neyman, and their foundational work on frequentist statistical theory and practice. In part, the amici correctly identified the experimental milieu in which Fisher worked, but the description of Fisher’s work is neither accurate nor fair. Fisher spent a lifetime thinking and writing about statistical tests, in much more nuanced ways than implied by the claim that such testing occurred in context of agricultural and quality-control experiments. Although Fisher worked on agricultural experiments, his writings acknowledged that when statistical tests and analyses were applied to observational studies, much more searching analyses of bias and confounding were required. Fisher’s and Berkson’s reactions to the observational studies of Hill and Doll on smoking and lung cancer are telling in this regard. These statisticians criticized the early smoking lung cancer studies, not for lack of statistical significance, but for failing to address confounding by a potential common genetic propensity to smoke and to develop lung cancer.

Questionable History of Drug Development

Twice in Rothman’s amicus brief, the authors suggest that “undue reliance” on statistical significance has resulted in overlooking “effective new treatments” because observed benefits were considered “not significant,” despite an “indication” of efficacy.¹⁴ The brief never provided any insight on what is due reliance and what is undue reliance on statistical significance. Their criticism of “undue reliance” implies that there are modes or instances of “due reliance” upon statistical significance. The amicus brief fails also to inform readers exactly what “effective new treatments” have been overlooked because the outcomes were considered “not significant.” This omission is regrettable because it leaves the reader with only abstract recommendations, without concrete examples of what such effective treatments might be. The omission was unfortunate because Rothman almost certainly could have marshalled examples. Recently, Rothman tweeted just such an example:¹⁵

“30% ↓ in cancer risk from Vit D/Ca supplements ignored by authors & editorial. Why? P = 0.06. http://bit.ly/2oanl6w http://bit.ly/2p0CRj7. The 95% confidence interval for the risk ratio was 0.42–1.02.”

Of course, this was a large, carefully reported randomized clinical trial, with a narrow confidence interval that just missed “statistical significance.” It is not an example that would have given succor to Bendectin plaintiffs, who were attempting to prove an association by identifying flaws in noisy observational studies that generally failed to show an association.

Readers of the 1992 amicus brief can only guess at what might be “indications of efficacy”; no explanation or examples are provided.¹⁶ The reality of FDA approvals of new drugs is that pre-specified 5% level of statistical significance is virtually always enforced.¹⁷ If a drug sponsor has “indication of efficacy,” it is, of course, free to follow up with an additional, larger, better-designed clinical trial. Rothman’s recent tweet about the vitamin D clinical trial does provide some context and meaning to what the amici may have meant over 25 years ago by indication of efficacy. The tweet also illustrates Rothman’s acknowledgment of the need to address random variability in a data set, whether by p-value or confidence interval, or both. Clearly, Rothman was criticizing the authors of the vitamin D trial for stopping short of claiming that they had shown (or “demonstrated”) a cancer survival benefit. There is, however, a rich literature on vitamin D and cancer outcomes, and such a claim could be made, perhaps, in the context of a meta-analysis or meta-regression of multiple clinical trials, with a synthesis of other experimental and observational data.¹⁸

Questionable History of Statistical Analyses in Epidemiology

Rothman’s amicus brief deserves credit for introducing a misinterpretation of Sir Austin Bradford Hill’s famous paper on inferring causal associations, which has become catechism in the briefs of plaintiffs in pharmaceutical and other products liability cases:

“No formal tests of significance can answer those questions. Such tests can, and should, remind us of the effects that the play of chance can create, and they will instruct us in the likely magnitude of those effects. Beyond that they contribute nothing to the ‘proof’ of our hypothesis.”

Austin Bradford Hill, “The Environment and Disease: Association or Causation?” 58 Proc. Royal Soc’y Med. 295, 290 (1965) (quoted at Rothman Brief at *6).

As exegesis of Hill’s views, this quote is misleading. The language quoted above was used by Hill in the context of his nine causal viewpoints or criteria. The Rothman brief ignores Hill’s admonition to his readers, that before reaching the nine criteria, there is a serious, demanding predicate that must be shown:

“Disregarding then any such problem in semantics we have this situation. Our observations reveal an association between two variables, perfectly clear-cut and beyond what we would care to attribute to the play of chance. What aspects of that association should we especially consider before deciding that the most likely interpretation of it is causation?”

Id. at 295 (emphasis added). Rothman and co-authors did not have to invoke the prestige and authority of Sir Austin, but once they did, they were obligated to quote him fully and with accurate context. Elsewhere, in his famous textbook, Hill expressed his view that common sense was insufficient to interpret data, and that the statistical method was necessary to interpret data in medical studies.¹⁹

Rothman complains that statistical significance focuses the reader on conjecture on the role of chance in the observed data rather than the information conveyed by the data themselves.²⁰ The “incompleteness” of statistical analysis for arriving at causal conclusions, however, is not an argument against its necessity.

The Rothman brief does make the helpful point that statistical significance cannot be sufficient to support a conclusion of causation because many statistically significant associations or correlations will be non-causal. They give a trivial example of wearing dresses and breast cancer, but the point is well-taken. Associations, even when statistically significant, are not necessarily causal conclusions. Who ever suggested otherwise, other than expert witnesses for the litigation industry?

Unnecessary Fears

The motivation for Rothman’s challenge to the assumption that statistical significance is necessary is revealed at the end of the argument on Point I. The authors plainly express their concern that false negatives will shut down important research:

“To give weight to the failure of epidemiological studies to meet strict ‘statistical significant’ standards — to use such studies to close the door on further inquiry — is not good science.”²¹

The relevance of this concern to the proceedings is a mystery. The judicial decisions in the case are not referenda on funding initiatives. Scientists were as free in 1993, after Daubert was decided, as they were in 1992, when Rothman wrote, to pursue the hypothesis that Bendectin caused birth defects. The decision had the potential to shut down tort claims, and left scientists to their tasks.

Reanalyses Are Appropriate Scientific Tools to Assess and Evaluate Data, and to Forge Causal Opinions

The Rothman brief took issue with the lower courts’ dismissal of plaintiffs’ expert witnesses’ re-analyses of data in published studies. The authors argued that reanalyses were part of the scientific method, and not “an arcane or specialized enterprise,” deserving of heightened or skeptical scrutiny.²²

Remarkably, the Rothman brief, if accepted by the Supreme Court on the re-analysis point, would have led to the sort of unthinking blanket acceptance of a methodology, which the brief’s authors condemned in the context of blanket acceptance of significance testing. The brief covertly urges “blind deference” to its authors on the blanket approval of re-analyses.

Although amici have tight page limits, the brief’s authors made clear that they were offering no substantive opinions on the data involved in the published epidemiologic studies on Bendectin, or on the plaintiffs’ expert witnesses’ re-analyses. With the benefit of hindsight, we can see that the sweeping language used by the Ninth Circuit on re-analyses might have been taken to foreclose important and valid meta-analyses or similar approaches. The Rothman brief is not terribly explicit on what re-analysis techniques were part of the scientific method, but meta-analyses surely had been on the authors’ minds:

“by focusing on inappropriate criteria applied to determine what conclusions, if any, can be reached from any one study, the trial court forecloses testimony about inferences that can be drawn from the combination of results reported by many such studies, even when those studies, standing alone, might not justify such inferences.”²³

The plaintiffs’ statistical expert witness in Daubert had proffered a re-analysis of at least one study by substituting a different control sample, as well as a questionable meta-analyses. By failing to engage on the propriety of the specific analyses at issue in Daubert, the Rothman brief failed to offer meaningful guidance to the appellate court.

Reanalyses Are Not Invalid Just Because They Have Not Been Published

Rothman was certainly correct that the value of peer review was overstated by the defense in Bendectin litigation.²⁴ The quality of pre-publication peer review is spotty, at best. Predatory journals deploy a pay-to-play scheme, which makes a mockery of scientific publishing. Even at respectable journals, peer review cannot effectively guard against fraud, or ensure that statistical analyses have been appropriately done.²⁵ At best, peer review is a weak proxy for study validity, and an unreliable one at that.

The Rothman brief may have moderated the Supreme Court’s reaction to the defense’s argument that peer review is a requirement for studies, or “re-analyses,” relied upon by expert witnesses. The Court in Daubert opined, in dicta, that peer review is a non-dispositive consideration:

“The fact of publication (or lack thereof) in a peer reviewed journal … will be a relevant, though not dispositive, consideration in assessing the scientific validity of a particular technique or methodology on which an opinion is premised.”²⁶

To the extent that Rothman and colleagues might have been disappointed in this outcome, they missed some important context of the Bendectin cases. Most of the cases had been resolved by a consolidated causation issues trial, but many opt-out cases had to be tried in state and federal courts around the country.²⁷ The expert witnesses challenged in Daubert (Drs. Swan and Done) participated in many of these opt-out cases, and in each case, they opined that Bendectin was a public health hazard. The failure of these witnesses to publish their analyses and re-analyses spoke volumes about their bona fides. Courts (and juries if the Swan and Done proffered testimony were admissible) could certainly draw negative inferences from the plaintiffs’ expert witnesses’ failure to publish their opinions and re-analyses.

The Fate of the “Rothman Approach” in the Courts

The so-called “Rothman approach” was urged by Bendectin plaintiffs in opposing summary judgment in a case pending in federal court, in New Jersey, before the Supreme Court decided Daubert. Plaintiffs resisted exclusion of their expert witnesses, who had relied upon inconsistent and statistically non-significant studies on the supposed teratogenicity of Bendectin. The trial court excluded the plaintiffs’ witnesses, and granted summary judgment.²⁸

On appeal, the Third Circuit reversed and remanded the DeLucas’s case for a hearing under Rule 702:

“by directing such an overall evaluation, however, we do not mean to reject at this point Merrell Dow’s contention that a showing of a .05 level of statistical significance should be a threshold requirement for any statistical analysis concluding that Bendectin is a teratogen regardless of the presence of other indicia of reliability. That contention will need to be addressed on remand. The root issue it poses is what risk of what type of error the judicial system is willing to tolerate. This is not an easy issue to resolve and one possible resolution is a conclusion that the system should not tolerate any expert opinion rooted in statistical analysis where the results of the underlying studies are not significant at a .05 level.”²⁹

After remand, the district court excluded the DeLuca plaintiffs’ expert witnesses, and granted summary judgment, based upon the dubious methods employed by plaintiffs’ expert witnesses in cherry picking data, recalculating risk ratios in published studies, and ignoring bias and confounding in studies. The Third Circuit affirmed the judgment for Merrell Dow.³⁰

In the end, the decisions in the DeLuca case never endorsed the Rothman approach, although Professor Rothman can take credit perhaps for forcing the trial court, on remand, to come to grips with the informational content of the study data, and the many threats to validity, which severely undermined the relied-upon studies and the plaintiffs’ expert witnesses’ opinions.

More recently, in litigation over alleged causation of birth defects in offspring of mothers who used Zoloft during pregnancy, plaintiffs’ counsel attempted to resurrect, through their expert witnesses, the Rothman approach. The multidistrict court saw through counsel’s assertions that the Rothman approach had been adopted in DeLuca, or that it had become generally accepted.³¹ After protracted litigation in the Zoloft cases, the district court excluded plaintiffs’ expert witnesses and entered summary judgment for the defense. The Third Circuit found that the district court’s handling of the statistical significance issues was fully consistent with the Circuit’s previous pronouncements on the issue of statistical significance.³²

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This entry was posted on Saturday, November 17th, 2018 at 12:10 pm and is filed under Causation, Rule 702, statistical evidence. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.