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

Reinventing the Burden of Proof

April 27th, 2016

If lawyers make antic claims that keep the courtrooms busy, law professors make antic proposals to suggest that the law is conceptually confused and misguided, to keep law reviews full.

A few years ago, an article by Professor Edward Cheng claimed that common law courts have failed to grasp the true meaning of burdens of proof. Edward K. Cheng, “Reconceptualizing the Burden of Proof,” 122 Yale L. J. 1254 (2013) [Cheng]. Every law student knows that the preponderance-of-the-evidence standard requires that the party with the burden of proof to establish each element of the claim or defense to a probability greater than 50%. Cheng acknowledges that courts know this as well (citations omitted), but then he goes on to state some remarkable assertions.

First, Cheng suggests that the legal system has engaged in a “casual recharacterization of the burden of proof into p > 0.5 and p > 0.95.” Cheng at 1258. Being charitable, let’s say “characterization” rather than “recharacterization,” for Cheng cites nothing for his suggestion that there was some prior characterization that the law mischievously changed. Cheng at 1258.

Second, Cheng claims that the failure to deal with quantified posterior probabilities is the result of an educational or psychological deficiency of judges and lawyers:

“By comparison, the criminal beyond-a-reasonable-doubt standard is akin to a probability greater than 0.9 or 0.95. Perhaps, as most courts have ruled, the prosecution is not allowed to quantify ‘reasonable doubt’, but that is only an odd quirk of the math-phobic legal system.”

Cheng at 1256 (internal citations omitted). Cheng’s “recharacterization” has given way to his own mischaracterization of the legal system. There is a pandemic math phobia in the legal system, but the refusal to quantify the burden of proof in criminal cases has nothing to do with fear or mathematical incompetence. Most cases simply do not permit any rational or principled quantification of posterior probabilities. And even if they were to allow such a cognitive maneuver, most people, and even judges, cannot map practical certainty, or something like “beyond a reaonable doubt” on to a probability scale of 0 to 1. No less than Judge Jack Weinstein, certainly a friend to the notion that “all evidence is probabilistic,” showed in his informal survey of federal judges of the Eastern District of New York, that judges have no idea of what probability corresponds to the criminal burden of proof:

US v Fatico BoP

U.S. v. Fatico, 458 F.Supp. 388 (E.D.N.Y. 1978). Judge Weinstein’s informal survey showed well enough that there is no real understanding of how to map reasonable doubt or its complement onto a scale of 0 to 1. Furthermore, for the vast majority of cases, there is simply no way to assign meaningful probabilities to events, causes, and states of mind, which make up the elements of claims and defenses in our legal system.

Third, Cheng makes much of the non-existence of absolute probabilities in legal contexts. The word “absolute” is used 14 times in his essay. This point is confusing as stated because no one, to my knowledge, has claimed that the burden of proof is an absolute probability that is stated or arrived at independently of evidence in the case. Plaintiffs and defendants can have burdens of proof and claims and defenses, respectively, but for sake of simplicity, let’s follow Cheng and describe the civil burden of proof as the plaintiff’s burden. The relevant probability is not the absolute probability P(Hπ), but rather the conditional posterior probability: P(Hπ | E).

Fourth, Cheng’s principal innovation, the introduction of a probability ratio as the true meaning and model of the burden of proof has little or no support in case law or in evidence theory. Cheng cites virtually no cases, and only a few selected publications from the world of law reviews. Cheng proposes to recast burdens of proof as a ratio of conditional probabilities of the plaintiff’s and defendant’s “stories.” If the posterior probability of the plaintiff’s story at trial’s end is P(Hπ | E)¹, and the defendant’s story is represented as P(Hδ | E), then Cheng argues that the plaintiff has carried his burden of proof whenever

P(Hπ | E) / P(Hδ | E) > 1.0

This innovation seems fundamentally wrong for several reasons. Again, assuming that the plaintiff or the State has the burden of proof, the defendant has none. If the plaintiff presents no evidence, then the numerator will be zero, and the ratio will be zero. The defendant prevails, and Cheng’s theory holds. But if the plaintiff presents some evidence and the defendant presents none, then the ratio is undefined. Alternatively, we may see the ratio in this situation as approaching infinity as a limit as the probability of the defendant’s “story” based upon his evidence approaches zero. On either interpretation of this scenario, the ratio Cheng invents is huge, and yet the plaintiff may well lose as for instance when plaintiff’s case is insufficient as a matter of law.

Cheng’s ratio theory thus fails as a descriptive theory. The theory appears to fail prescriptively as well. In most civil and criminal cases, the finder of fact is instructed that the defendant has no burden of proof and need not present any evidence at all. Even when the defendant has remained silent, and the plaintiff has presented a legally sufficient case, the fact finder may return a verdict for the defendant when the P(Hπ | E) seems too low with respect to the burden of proof.

Let’s consider an example, perhap not too far fetched in some American courtrooms. The plaintiff claims that drug A has caused him to develop Syndrome Z. Plaintiff has no clinical trial, or analytical epidemiologic, or animal evidence to support his claim. All the plaintiff can adduce is a so-called disproportionality analysis based upon the reporting of adverse events to the FDA. The defendant does not present any evidence of safety. The end point of interest in the lawsuit, Syndrome Z, was not observed in the trials, and was never looked for in any epidemiologic or toxicologic study. The defendant thus has no affirmative evidence of safety that counts for P(Hδ | E).

Assuming that the trial court does not toss this claim pretrial on a Rule 702 motion, or on a directed verdict, the defendant must address the plaintiff’s claim and the assertion that P(Hπ | E) > 0. The plaintiff supports his claim and assertion by presenting an expert witness who endorses the validity, accuracy, and probativeness of the disproportionality analysis. The defendant confronts this evidence solely on cross-examination, and not by trying to suggest that the plaintiff’s expert witness’s analysis is actually evidence of safety. The point of the cross-examination is to show that the proferred analysis is not a valid tool and lacks validity, accuracy, and probativeness.

In this situation, the plaintiff’s P(Hπ | E) might have been greater than 0.5 at the end of direct examination, but if defense counsel has done his job, then at the end of the cross-examination, the P(Hπ | E) < 0.5. Perhaps at this stage of the proceedings, P(Hπ | E) < 0.01.

The defendant, having no affirmative evidence of safety, rests without presenting any evidence. P(Hδ | E) = 0. Alas, we cannot say that P(Hδ | E) is the complement of P(Hπ | E). There is, in most cases, way too much room for ignorance, indeterminate, or unknown probability of the P(Hδ). In this hypothetical, however, there is no evidence adduced for safety at all, only very weak and unreliable evidence of harm. The ratio is undefined, but the law would allow the dismissal of the plaintiff’s case, or would affirm a rational fact finder’s return of a defense verdict. And the law should do those things.

Fifth, Cheng commits other errors along the way to arriving at his ratio theory. In one instance, he commits a serious category mistake:

“Looking at the statistical world, we immediately see that characterizing any decision rule as a 0.5 probability threshold is odd. Statisticians rarely attempt to prove the truth of a proposition or hypothesis by using its absolute probability. Instead, hypothesis testing is usually comparative. There is a null hypothesis and an alternative hypothesis, and one is rejected in favor of the other depending on the evidence observed and the consistency of that evidence with the two hypotheses.”

Cheng at 1259 (internal citations omitted; emphasis added).

Again, Cheng is correct insofar as he suggests that statisticians do not often use use absolute probabilities. Attained levels of significance probabilities, whether used in hypothesis testing or otherwise, are conditional probabilities that describe the probability of observing the sample statistic, or one more extreme, based upon the statistical model and posited null hypothesis. Indeed, many methodologically rigorous statisticians and scientists would resist placing a quantified posterior probability on the truth of a proposition or hypothesis. The measures of probability may be helpful in identifying uncertainties due to random error, or even on occasion due to bias, but these measures do not translate into assigning the quantified posterior probabilites that Cheng wants and needs to make his ratio theory work. There is nothing, however, odd about using the quantified posterior probability of greater than 50% as a metaphor.

But whence comes rejecting one hypothesis “in favor of” another, as a matter of statistics? The null hypothesis is not accepted in the hypothesis test; rather it was assumed in order to conduct the test. The inference Cheng describes would be improper. In a footnote, Cheng asserts that “classical hypothesis testing strongly favors the null hypothesis,” but this conflates attained level of significance with posterior probabilities. Cheng at 1259 n. 12. Cheng states that “the null hypothesis can be given no specific preference,” in legal contexts, id., but this statement seems to ignore what it means for a party to have a burden of proving facts needed to establish its claim or defense.

Of course, over the course of multiple studies, which look at the issue repeatedly with increasingly precise and valid experiments and studies, and which consistently fail to reject a given null hypothesis, we sometimes do, as a matter of judgment, accept the null hypothesis. This situation has little to do with the Cheng’s ratio theory, however.

1 Where P stands for probability, Hπ for the plaintiff’s “story,” Hδ for the defendant’s story, P(Hπ | E) represents the posterior probability at trial’s end of the plaintiff’s story given the evidence, and P(Hδ | E) represents the posterior probability at trial’s end of the defendant’s story given the evidence.

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Lipitor Diabetes MDL’s Inexact Analysis of Fisher’s Exact Test

April 21st, 2016

Muriel Bristol was a biologist who studied algae at the Rothamsted Experimental Station in England, after World War I. In addition to her knowledge of plant biology, Bristol claimed the ability to tell whether tea had been added to milk, or the tea poured first and then milk had been added. Bristol, as a scientist and a proper English woman, preferred the latter.

Ronald Fisher, who also worked at Rothamsted, expressed his skepticism over Dr. Bristol’s claim. Fisher set about to design a randomized experiment that would efficiently and effectively test her claim. Bristol was presented with eight cups of tea, four of which were prepared with milk added to tea, and four prepared with tea added to milk. Bristol, of course, was blinded to which was which, but was required to label each according to its manner of preparation. Fisher saw his randomized experiment as a 2 x 2 contingency table, from he could calculate the observed outcome (and ones more extreme if there were any more extreme outcomes) using the assumption of fixed marginal rates and the hypergeometric probability distribution. Fisher’s Exact Test was born at tea time.[1]

Fisher described the origins of his Exact Test in one of his early texts, but he neglected to report whether his experiment vindicated Bristol’s claim. According to David Salsburg, H. Fairfield Smith, one of Fisher’s colleagues, acknowledged that Bristol nailed Fisher’s Exact test, with all eight cups correctly identified. The test has gone on to become an important tool in the statistician’s armamentarium.

Fisher’s Exact, like any statistical test, has model assumptions and preconditions. For one thing, the test is designed for categorical data, with binary outcomes. The test allows us to evaluate whether two proportions are likely different by chance alone, by calculating the probability of the observed outcome, as well as more extreme outcomes.

The calculation of an exact attained significance probability, using Fisher’s approach, provides a one-sided p-value, with no unique solution to calculating a two-side attained significance probability. In discrimination cases, the one-sided p-value may well be more appropriate for the issue at hand. The Fisher’s Exact Test has thus played an important role in showing the judiciary that small sample size need not be an insuperable barrier to meaningful statistical analysis. In discrimination cases, the one-sided p-value provided by the test is not a particular problem.[2]

The difficulty of using Fisher’s Exact for small sample sizes is that the hypergeometric distribution, upon which the test is based, is highly asymmetric. The observed one-sided p-value does not measure the probability of a result equally extreme in the opposite direction. There are at least three ways to calculate the p-value:

Double the one-sided p-value.
Add the point probabilities from the opposite tail that are more extreme than the observed point probability.
Use the mid-P value; that is, add all values more extreme (smaller) than the observed point probability from both sides of the distribution, PLUS ½ of the observed point probability.

Some software programs will proceed in one of these ways by default, but their doing so does guarantee the most accurate measure of two-tailed significance probability.

In the Lipitor MDL for diabetes litigation, Judge Gergel generally used sharp analyses to cut through the rancid fat of litigation claims, to get to the heart of the matter. By and large, he appears to have done a splendid job. In course of gatekeeping under Federal Rule of Evidence 702, however, Judge Gergel may have misunderstood the nature of Fisher’s Exact Test.

Nicholas Jewell is a well-credentialed statistician at the University of California. In the courtroom, Jewell is a well-known expert witness for the litigation industry. He is no novice at generating unreliable opinion testimony. See In re Zoloft Prods. Liab. Litig., No. 12–md–2342, 2015 WL 7776911 (E.D. Pa. Dec. 2, 2015) (excluding Jewell’s opinions as scientifically unwarranted and methodologically flawed). In the Lipitor cases, some of Jewell’s opinions seemed outlandish indeed, and Judge Gergel generally excluded them. See In re Lipitor Marketing, Sales Practices and Prods. Liab. Litig., MDL No. 2:14-mn-02502-RMG, ___ F.Supp. 3d ___ (2015), 2015 WL 7422613 (D.S.C. Nov. 20, 2015) [Lipitor Jewell], reconsideration den’d, 2016 WL 827067 (D.S.C. Feb. 29, 2016) [Lipitor Jewell Reconsidered].

As Judge Gergel explained, Jewell calculated a relative risk for abnormal blood glucose in a Lipitor group to be 3.0 (95% C.I., 0.9 to 9.6), using STATA software. Also using STATA, Jewell obtained an attained significance probability of 0.0654, based upon Fisher’s Exact Test. Lipitor Jewell at *7.

Judge Gergel did not report whether Jewell’s reported p-value of 0.0654, was one- or two-sided, but he did state that the attained probability “indicates a lack of statistical significance.” Id. & n. 15. The rest of His Honor’s discussion of the challenged opinion, however, makes clear that of 0.0654 must have been a two-sided value. If it had been a one-sided p-value, then there would have been no way of invoking the mid-p to generate a two-sided p-value below 5%. The mid-p will always be larger than the one-tailed exact p-value generated by Fisher’s Exact Test.

The court noted that Dr. Jewell had testified that he believed that STATA generated this confidence interval by “flip[ping]” the Taylor series approximation. The STATA website notes that it calculates confidence intervals for odds ratios (which are different from the relative risk that Jewell testified he computed), by inverting the Fisher exact test.[3] Id. at *7 & n. 17. Of course, this description suggests that the confidence interval is not based upon exact methods.

STATA does not provide a mid p-value calculation, and so Jewell used an on-line calculator, to obtain a mid p-value of 0.04, which he declared statistically significant. The court took Jewell to task for using the mid p-value as though it were a different analysis or test. Id. at *8. Because the mid-p value will always be larger than the one-sided exact p-value from Fisher’s Exact Test, the court’s explanation does not really make sense:

“Instead, Dr. Jewell turned to the mid-p test, which would ‘[a]lmost surely’ produce a lower p-value than the Fisher exact test.”

Id. at *8. The mid-p test, however, is not different from the Fisher’s exact; rather it is simply a way of dealing with the asymmetrical distribution that underlies the Fisher’s exact, to arrive at a two-tailed p-value that more accurately captures the rate of Type I error.

The MDL court acknowledged that the mid-p approach, was not inherently unreliable, but questioned Jewell’s inconsistent, selective use of the approach for only one test.[4] Jewell certainly did not help the plaintiffs’ cause and his standing by having discarding the analyses that were not incorporated into his report, thus leaving the MDL court to guess at how much selection went on in his process of generating his opinions.. Id. at *9 & n. 19.

None of Jewell’s other calculated p-values involved the mid-p approach, but the court’s criticism begs the question whether the other p-values came from a Fisher’s Exact Test with small sample size, or other highly asymmetrical distribution. Id. at *8. Although Jewell had shown himself willing to engage in other dubious, result-oriented analyses, Jewell’s use of the mid-p for this one comparison may have been within acceptable bounds after all.

The court also noted that Jewell had obtained the “exact p-value and that this p-value was not significant.” Id. The court’s notation here, however, does not report the important detail whether that exact, unreported p-value was merely the doubled of the one-sided p-value given by the Fisher’s Exact Test. As the STATA website, cited by the MDL court, explains:

“The test naturally gives a one-sided p-value, and there are at least four different ways to convert it to a two-sided p-value (Agresti 2002, 93). One way, not implemented in Stata, is to double the one-sided p-value; doubling is simple but can result in p-values larger than one.”

Wesley Eddings, “Fisher’s exact test two-sided idiosyncrasy” (Jan. 2009) (citing Alan Agresti, Categorical Data Analysis 93 (2d ed. 2002)).

On plaintiffs’ motion for reconsideration, the MDL court reaffirmed its findings with respect to Jewell’s use of the mid-p. Lipitor Jewell Reconsidered at *3. In doing so, the court insisted that the one instance in which Jewell used the mid-p stood in stark contrast to all the other instances in which he had used Fisher’s Exact Test. The court then cited to the record to identify 21 other instances in which Jewell used a p-value rather than a mid-p value. The court, however, did not provide the crucial detail whether these 21 other instances actually involved small-sample applications of Fisher’s Exact Test. As result-oriented as Jewell can be, it seems safe to assume that not all his statistical analyses involved Fisher’s Exact Test, with its attendant ambiguity for how to calculate a two-tailed p-value.

Post-Script (Aug. 9, 2017)

The defense argument and the judicial error were echoed in a Washington Legal Foundation paper that pilloried Nicholas Jewell for the surfeit of many methodological flaws in his expert witness opinions in In re Lipitor. Unfortunately, the paper uncritically recited the defense’s theory about the Fisher’s Exact Test:

“In assessing Lipitor data, even after all of the liberties that [Jewell] took with selecting data, he still could not get a statistically-significant result employing a Fisher’s exact test, so he switched to another test called a mid-p test, which generated a (barely) statistically significant result.”

Kirby Griffis, “The Role of Statistical Significance in Daubert/Rule 702 Hearings,” at 19, Wash. Leg. Foundation Critical Legal Issues Working Paper No. 201 (Mar. 2017). See Kirby Griffis, “Beware the Weak Argument: The Rule of Thirteen,” For the Defense 72 (July 2013) (quoting Justice Frankfurter, “A bad argument is like the clock striking thirteen. It puts in doubt the others.”). The fallacy of Griffis’ argument is that it assumes that a mid-p calculation is a different statistical test from the Fisher’s Exact test, which yields a one-tailed significance probability. Unfortunately, Griffis’ important paper is marred by this and other misstatements about statistics.

[1] Sir Ronald A. Fisher, The Design of Experiments at chapter 2 (1935); see also Stephen Senn, “Tea for three: Of infusions and inferences and milk in first,” Significance 30 (Dec. 2012); David Salsburg, The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century (2002).

[2] See, e.g., Dendy v. Washington Hosp. Ctr., 431 F. Supp. 873 (D.D.C. 1977) (denying preliminary injunction), rev’d, 581 F.2d 99 (D.C. Cir. 1978) (reversing denial of relief, and remanding for reconsideration). See also National Academies of Science, Reference Manual on Scientific Evidence 255 n.108 (3d ed. 2011) (“Well-known small sample techniques [for testing significance and calculating p-values] include the sign test and Fisher’s exact test.”).

[3] See Wesley Eddings, “Fisher’s exact test two-sided idiosyncrasy” (Jan. 2009), available at <http://www.stata.com/support/faqs/statistics/fishers-exact-test/>, last visited April 19, 2016 (“Stata’s exact confidence interval for the odds ratio inverts Fisher’s exact test.”). This article by Eddings contains a nice discussion of why the Fisher’s Exact Test attained significance probability disagrees with the calculated confidence interval. Eddings points out the asymmetry of the hypergeometric distribution, which complicates arriving at an exact p-value for a two-sided test.

[4] See Barber v. United Airlines, Inc., 17 Fed.Appx. 433, 437 (7th Cir. 2001) (“Because in formulating his opinion Dr. Hynes cherry-picked the facts he considered to render an expert opinion, the district court correctly barred his testimony because such a selective use of facts fails to satisfy the scientific method and Daubert.”).

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The Education of Judge Rufe – The Zoloft MDL

April 9th, 2016

The Honorable Cynthia M. Rufe is a judge on the United States District Court, for the Eastern District of Pennsylvania. Judge Rufe was elected to a judgeship on the Bucks County Court of Common Pleas in 1994. She was appointed to the federal district court in 2002. Like most state and federal judges, little in her training and experience as a lawyer prepared her to serve as a gatekeeper of complex expert witness scientific opinion testimony. And yet, the statutory code of evidence, and in particular, Federal Rules of Evidence 702 and 703, requires her do just that.

The normal approach to MDL cases is marked by the Field of Dreams: “if you build it, they will come.” Last week, Judge Rufe did something that is unusual in pharmaceutical litigation; she closed the gate and sent everyone home. In re Zoloft Prod. Liab. Litig., MDL NO. 2342, 12-MD-2342, 2016 WL 1320799 (E.D. Pa. April 5, 2016).

Her Honor’s decision was hardly made in haste. The MDL began in 2012, and proceeded in a typical fashion with case management orders that required the exchange of general causation expert witness reports. The plaintiffs’ steering committee (PSC), acting for the plaintiffs, served the report of only one epidemiologist, Anick Bérard, who took the position that Zoloft causes virtually every major human congenital anomaly known to medicine. The defendants challenged the admissibility of Bérard’s opinions. After extensive briefings and evidentiary hearings, the trial court found that Bérard’s opinions were riddled with inconsistent assessments of studies, eschewed generally accepted methods of causal inference, ignored contrary evidence, adopted novel, unreliable methods of endorsing “trends” in studies, and failed to address epidemiologic studies that did not support her subjective opinions. In re Zoloft Prods. Liab. Litig., 26 F. Supp. 3d 449 (E.D.Pa.2014). The trial court permitted plaintiffs an opportunity to seek reconsideration of Bérard’s exclusion, which led to the trial court’s reaffirming its previous ruling. In re Zoloft Prods. Liab. Litig., No. 12–md–2342, 2015 WL 314149, at *2 (E.D.Pa. Jan. 23, 2015).

Notwithstanding the PSC’s claims that Bérard was the best qualified expert witness in her field and that she was the only epidemiologist needed to support the plaintiffs’ causal claims, the MDL court indulged the PSC by permitting plaintiffs another bite at the apple. Over defendants’ objections, the court permitted the PSC to name yet another expert witness, statistician Nicholas Jewell, to do what Bérard had failed to do: proffer an opinion on general causation supported by sound science. In re Zoloft Prods. Liab. Litig., No. 12–md–2342, 2015 WL 115486, at * 2 (E.D.Pa. Jan. 7, 2015).

As a result of this ruling, the MDL dragged on for over a year, in which time, the PSC served a report by Jewell, and then the defendants conducted a discovery deposition of Jewell, and lodged a new Rule 702 challenge. Although Jewell brought more statistical sophistication to the task, he could not transmute lead into gold; nor could he support the plaintiffs’ causal claims without committing most of the same fallacies found in Bérard’s opinions. After another round of Rule 702 briefs and hearings, the MDL court excluded Jewell’s unwarranted causal opinions. In re Zoloft Prods. Liab. Litig., No. 12–md–2342, 2015 WL 7776911 (E.D.Pa. Dec. 2, 2015).

The successive exclusions of Bérard and Jewell left the MDL court in a peculiar position. There were other witnesses, Robert Cabrera, a teratologist, Michael Levin, a molecular biologist, and Thomas Sadler, an embryologist, whose opinions addressed animal toxicologic studies, biological plausibility, and putative mechanisms. These other witnesses, however, had little or no competence in epidemiology, and they explicitly relied upon Bérard’s opinions with respect to human outcomes. As a result of Bérard’s exclusion, these witnesses were left free to offer their views about what happens in animals at high doses, or about theoretical mechanisms, but they were unable to address human causation.

Although the PSC had no expert witnesses who could legitimately offer reasonably supported opinions about the causation of human birth defects, the plaintiffs refused to decamp and leave the MDL forum. Faced with the prospect of not trying their cases to juries, the PSC instead tried the patience of the MDL judge. The PSC pulled out the stops in adducing weak, irrelevant, and invalid evidence to support their claims, sans epidemiologic expertise. The PSC argued that adverse event reports, internal company documents that discussed possible associations, the biological plausibility opinions of Levin and Sadler, the putative mechanism opinions of Cabrera, differential diagnoses offered to support specific causation, and the hip-shot opinions of a former-FDA-commissioner-for-hire, David Kessler could come together magically to supply sufficient evidence to have their cases submitted to juries. Judge Rufe saw through the transparent effort to manufacture evidence of causation, and granted summary judgment on all remaining Zoloft cases in the MDL. s In re Zoloft Prod. Liab. Litig., MDL NO. 2342, 12-MD-2342, 2016 WL 1320799, at *4 (E.D. Pa. April 5, 2016).

After a full briefing and hearing on Bérard’s opinion, a reconsideration of Bérard, a permitted “do over” of general causation with Jewell, a full briefing and hearing on Jewell’s opinions, the MDL court was able to deal deftly with the snippets of evidence “cobbled together” to substitute for evidence that might support a conclusion of causation. The PSC’s cobbled case was puffed up to give the appearance of voluminous evidence, in 200 exhibits that filled six banker’s boxes. Id. at *5. The ruse was easily undone; most of the exhibits and purported evidence were obvious rubbish. “The quantity of the evidence is not, however, coterminous with the quality of evidence with regard to the issues now before the Court.” Id. The banker’s boxes contained artifices such as untranslated foreign-language documents, and company documents relating to the development and marketing of the medication. The PSC resubmitted reports from Levin, Cabrera, and Sadler, whose opinions were already adjudicated to be incompetent, invalid, irrelevant, or inadequate to support general causation. The PSC pointed to the specific causation opinions of a clinical cardiologist, Ra-Id Abdulla, M.D., who proffered dubious differential etiologies, ruling in Zoloft as a cause of individual children’s birth defects, despite his inability to rule out truly known and unknown causes in the differential reasoning. The MDL court, however, recognized that “[a] differential diagnosis assumes that general causation has been established,” id. at *7, and that Abdulla could not bootstrap general causation by purporting to reach a specific causation opinion (even if those specific causation opinions were legitimate).

The PSC submitted the recent consensus statement of the American Statistical Association (ASA)[1], which it misrepresented to be an epidemiologic study. Id. at *5. The consensus statement makes some pedestrian pronouncements about the difference between statistical and clinical significance, about the need for other considerations in addition to statistical significance, in supporting causal claims, and the lack of bright-line distinctions for statistical significance in assessing causality. All true, but immaterial to the PSC’s expert witnesses’ opinions that over-endorsed statistical significance in the few instances in which it was shown, and over-interpreted study data that was based upon data mining and multiple comparisons, in blatant violation of the ASA’s declared principles.

Stretching even further for “human evidence,” the PSC submitted documentary evidence of adverse event reports, as though they could support a causal conclusion.[2] There are about four million live births each year, with an expected rate of serious cardiac malformations of about one per cent.[3] The prevalence of SSRI anti-depressant use is at least two per cent, which means that we would expect 800 cardiac birth defects each year to occur in children of mother’s who took SSRI anti-depressants in the first trimester. If Zoloft had an average market share of all the SSRIs of about 25 per cent, then 200 cardiac defects each year would occur in children born to mothers who took Zoloft. Given that Zoloft has been on the market since the early 1990s, we would expect that there would be thousands of children, exposed to Zoloft during embryogenesis, born with cardiac defects, if there was nothing untoward about maternal exposure to the medication. Add the stimulated reporting of adverse events from lawyers, lawyer advertising, and lawyer instigation, you have manufactured evidence not probative of causation at all.[4] The MDL court cut deftly and swiftly through the smoke screen:

“These reports are certainly relevant to the generation of study hypotheses, but are insufficient to create a material question of fact on general causation.”

Id. at *9. The MDL court recognized that epidemiology was very important in discerning a causal connection between a common exposure and a common outcome, especially when the outcome has an expected rate in the general population. The MDL court stopped short of holding that epidemiologic evidence was required (which on the facts of the case would have been amply justified), but instead supported its ratio decidendi on the need to account for the extant epidemiology that contradicted or failed to support the strident and subjective opinions of the plaintiffs’ expert witnesses. The MDL court thus gave plaintiffs every benefit of the doubt by limiting its holding on the need for epidemiology to:

“when epidemiological studies are equivocal or inconsistent with a causation opinion, experts asserting causation opinions must thoroughly analyze the strengths and weaknesses of the epidemiological research and explain why that body of research does not contradict or undermine their opinion.”

Id. at *5, quoting from In re Zoloft Prods. Liab. Litig., 26 F. Supp. 3d 449, 476 (E.D. Pa. 2014).

The MDL court also saw through the thin veneer of respectability of the testimony of David Kessler, a former FDA commissioner who helped make large fortunes for some of the members of the PSC by the feeding frenzy he created with his moratorium on silicone gel breast implants. Even viewing Kessler’s proffered testimony in the most charitable light, the court recognized that he offered little support for a causal conclusion other than to delegate the key issues to epidemiologists. Id. at *9. As for the boxes of regulatory documents, foreign labels, and internal company memoranda, the MDL court found that these documents did not raise a genuine issue of material fact concerning general causation:

“Neither these documents, nor draft product documents or foreign product labels containing language that advises use of birth control by a woman taking Zoloft constitute an admission of causation, as opposed to acknowledging a possible association.”

Id.

In the end, the MDL court found that the PSC’s many banker boxes of paper contained too much of nothing for the issue at hand. Having put the defendants through the time and expense of litigating and re-litigating these issues, nothing short of dismissing the pending cases was a fair and appropriate outcome to the Zoloft MDL.

_______________________________________

Given the denouement of the Zoloft MDL, it is worth considering the MDL judge’s handling of the scientific issues raised, misrepresented, argued, or relied upon by the parties. Judge Rufe was required, by Rules 702 and 703, to roll up her sleeves and assess the methodological validity of the challenged expert witnesses’ opinions. That Her Honor was able to do this is a testament to her hard work. Zoloft was not Judge Rufe’s first MDL, and she clearly learned a lot from her previous judicial assignment to an MDL for Avandia personal injury actions.

On May 21, 2007, the New England Journal of Medicine published online a seriously flawed meta-analysis of cardiovascular disease outcomes and rosiglitazone (Avandia) use. See Steven E. Nissen, M.D., and Kathy Wolski, M.P.H., “Effect of Rosiglitazone on the Risk of Myocardial Infarction and Death from Cardiovascular Causes,” 356 New Engl. J. Med. 2457 (2007). The Nissen article did not appear in print until June 14, 2007, but the first lawsuits resulted within a day or two of the in-press version. The lawsuits soon thereafter reached a critical mass, with the inevitable creation of a federal court Multi-District Litigation.

Within a few weeks of Nissen’s article, the Annals of Internal Medicine published an editorial by Cynthia Mulrow, and other editors, in which questioned the Nissen meta-analysis[5], and introduced an article that attempted to replicate Nissen’s work[6]. The attempted replication showed that the only way Nissen could have obtained his nominally statistically significant result was to have selected a method, Peto’s fixed effect method, known to be biased for use with clinical trials with uneven arms. Random effect methods, more appropriate for the clinically heterogeneous clinical trials, consistently failed to replicate the Nissen result. Other statisticians weighed in and pointed out that using the risk difference made much more sense when there were multiple trials with zero events in one or the other or both arms of the trials. Trials with zero cardiovascular events in both arms represented important evidence of low, but equal risk, of heart attacks, which should be captured in an appropriate analysis. When the risk difference approach was used, with exact statistical methods, there was no statistically significant increase in risk in the dataset used by Nissen.[7] Other scientists, including some of Nissen’s own colleagues at the Cleveland Clinic, and John Ioannidis, weighed in to note how fragile and insubstantial the Nissen meta-analysis was[8]:

“As rosiglitazone case demonstrates, minor modifications of the meta-analysis protocol can change the statistical significance of the result. For small effects, even the direction of the treatment effect estimate may change.”

Nissen achieved his political objective with his shaky meta-analysis. The FDA convened an Advisory Committee meeting, which in turn resulted in a negative review of the safety data, and the FDA’s imposition of warnings and a Risk Evaluation and Mitigation Strategy, which all but prohibited use of rosiglizone.[9] A clinical trial, RECORD, had already started, with support from the drug sponsor, GlaxoSmithKline, which fortunately was allowed to continue.

On a parallel track to the regulatory activities, the federal MDL, headed by Judge Rufe, proceeded to motions and a hearing on GSK’s Rule 702 challenge to plaintiffs’ evidence of general causation. The federal MDL trial judge denied GSK’s motions to exclude plaintiffs’ causation witnesses in an opinion that showed significant diffidence in addressing scientific issues. In re Avandia Marketing, Sales Practices and Product Liability Litigation, 2011 WL 13576, *12 (E.D. Pa. 2011). See “Learning to Embrace Flawed Evidence – The Avandia MDL’s Daubert Opinion” (Jan. 10, 2011.

After Judge Rufe denied GSK’s challenges to the admissibility of plaintiffs’ expert witnesses’ causation opinions in the Avandia MDL, the RECORD trial was successfully completed and published.[10] RECORD was a long term, prospectively designed randomized cardiovascular trial in over 4,400 patients, followed on average of 5.5 yrs. The trial was designed with a non-inferiority end point of ruling out a 20% increased risk when compared with standard-of-care diabetes treatment The trial achieved its end point, with a hazard ratio of 0.99 (95% confidence interval, 0.85-1.16) for cardiovascular hospitalization and death. A readjudication of outcomes by the Duke Clinical Research Institute confirmed the published results.

On Nov. 25, 2013, after convening another Advisory Committee meeting, the FDA announced the removal of most of its restrictions on Avandia:

“Results from [RECORD] showed no elevated risk of heart attack or death in patients being treated with Avandia when compared to standard-of-care diabetes drugs. These data do not confirm the signal of increased risk of heart attacks that was found in a meta-analysis of clinical trials first reported in 2007.”

FDA Press Release, “FDA requires removal of certain restrictions on the diabetes drug Avandia” (Nov. 25, 2013). And in December 2015, the FDA abandoned its requirement of a Risk Evaluation and Mitigation Strategy for Avandia. FDA, “Rosiglitazone-containing Diabetes Medicines: Drug Safety Communication – FDA Eliminates the Risk Evaluation and Mitigation Strategy (REMS)” (Dec. 16, 2015).

GSK’s vindication came too late to reverse Judge Rufe’s decision in the Avandia MDL. GSK spent over six billion dollars on resolving Avandia claims. And to add to the company’s chagrin, GSK lost patent protection for Avandia in April 2012.[11]

Something good, however, may have emerged from the Avandia litigation debacle. Judge Rufe heard from plaintiffs’ expert witnesses in Avandia about the hierarchy of evidence, about how observational studies must be evaluated for bias and confounding, about the importance of statistical significance, and about how studies that lack power to find relevant associations may still yield conclusions with appropriate meta-analysis. Important nuances of meta-analysis methodology may have gotten lost in the kerfuffle, but given that plaintiffs had reasonable quality clinical trial data, Avandia plaintiffs’ counsel could eschew their typical reliance upon weak and irrelevant lines of evidence, based upon case reports, adverse event disproportional reporting, and the like.

The Zoloft litigation introduced Judge Rufe to a more typical pharmaceutical litigation. Because the outcomes of interest were birth defects, there were no clinical trials. To be sure, there were observational epidemiologic studies, but now the defense expert witnesses were carefully evaluating the studies for bias and confounding, and the plaintiffs’ expert witnesses were double counting studies and ignoring multiple comparisons and validity concerns. Once again, in the Zoloft MDL, plaintiffs’ expert witnesses made their non-specific complaints about “lack of power” (without ever specifying the relevant alternative hypothesis), but it was the defense expert witnesses who cited relevant meta-analyses that attempted to do something about the supposed lack of power. Plaintiffs’ expert witnesses inconsistently argued “lack of power” to disregard studies that had outcomes that undermined their opinions, even when those studies had narrow confidence intervals surrounding values at or near 1.0.

The Avandia litigation laid the foundation for Judge Rufe’s critical scrutiny by exemplifying the nature and quantum of evidence to support a reasonable scientific conclusion. Notwithstanding the mistakes made in the Avandia litigation, this earlier MDL created an invidious distinction with the Zoloft PSC’s evidence and arguments, which looked as weak and insubstantial as they really were.

[1] Ronald L. Wasserstein & Nicole A. Lazar, “The ASA’s Statement on p-Values: Context, Process, and Purpose,” The American Statistician, available online (Mar. 7, 2016), in-press at DOI:10.1080/00031305.2016.1154108, <http://dx.doi.org/10.1080/>. See “The American Statistical Association’s Statement on and of Significance” (Mar. 17, 2016); “The ASA’s Statement on Statistical Significance – Buzzing from the Huckabees” (Mar. 19, 2016).

[2] See 21 C.F.R. § 314.80 (a) Postmarketing reporting of adverse drug experiences (defining “[a]dverse drug experience” as “[a]ny adverse event associated with the use of a drug in humans, whether or not considered drug related”).

[3] See Centers for Disease Control and Prevention, “Birth Defects Home Page” (last visited April 8, 2016).

[4] See, e.g., Derrick J. Stobaugh, Parakkal Deepak, & Eli D. Ehrenpreis, “Alleged isotretinoin-associated inflammatory bowel disease: Disproportionate reporting by attorneys to the Food and Drug Administration Adverse Event Reporting System,” 69 J. Am. Acad. Dermatol. 393 (2013) (documenting stimulated reporting from litigation activities).

[5] Cynthia D. Mulrow, John Cornell & A. Russell Localio, “Rosiglitazone: A Thunderstorm from Scarce and Fragile Data,” 147 Ann. Intern. Med. 585 (2007).

[6] George A. Diamond, Leon Bax & Sanjay Kaul, “Uncertain Effects of Rosiglitazone on the Risk for Myocardial Infartion and Cardiovascular Death,” 147 Ann. Intern. Med. 578 (2007).

[7] Tian, et al., “Exact and efficient inference procedure for meta-analysis and its application to the analysis of independent 2 × 2 tables with all available data but without artificial continuity correction” 10 Biostatistics 275 (2008)

[8] Adrian V. Hernandez, Esteban Walker, John P.A. Ioannidis, and Michael W. Kattan, “Challenges in meta-analysis of randomized clinical trials for rare harmful cardiovascular events: the case of rosiglitazone,” 156 Am. Heart J. 23, 28 (2008).

[9] Janet Woodcock, FDA Decision Memorandum (Sept. 22, 2010).

[10] Philip D. Home, et al., “Rosiglitazone evaluated for cardiovascular outcomes in oral agent combination therapy for type 2 diabetes (RECORD): a multicentre, randomised, open-label trial,” 373 Lancet 2125 (2009).

[11] “Pharmacovigilantism – Avandia Litigation” (Nov. 27, 2013).

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