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

The Lawsuit Industry’s Cookbook for Bogus Epidemiology

November 15th, 2016

In 1965, Sir Austin Bradford Hill was appropriately elected to the President of the Royal Society of Medicine. Along with Sir Richard Doll and others, Hill had pioneered the use of epidemiologic and statistical methods, which in the 1950s, he had applied to the issue of tobacco smoking and lung cancer. By the late 1950s, Hill and Doll had embraced and urged a causal association between smoking and lung cancer, but they had formidable opponents in Joseph Berkson and Sir Ronald A. Fisher.

By 1964, Hill and Doll’s causal thesis had largely prevailed. On January 11, 1964, the Office of the Surgeon General, in the United States, issued a Committee report that reviewed the available evidence and concluded that the relationship between smoking and lung cancer was indeed causal. See Surgeon General’s Advisory Committee on Smoking and Health, Smoking and Health (Office of the Surgeon General, United States Public Health Service 1964). See alsoProfiles in Science – 1964 Report,” National Library of Medicine Website.

Almost a year to the day after the Surgeon General’s report was issued, Hill gave the President’s Address at the Royal Society of Medicine, in London. For Hill, by then Professor Emeritus of Medical Statistics in the University of London, the occasion was triumphant. Not only had he prevailed over the intellectual doubts of Berkson and Fisher, and the animadversions of the tobacco industry, but he had shown that causal relationships can be identified and established with statistical methods in population studies, even in the absence of demonstrated mechanisms or experimental randomization. Fittingly, his after-dinner speech outlined the methodology that had proved successful, and that speech was published in the Proceedings of the Royal Society of Medicine. Austin Bradford Hill, “The Environment and Disease: Association or Causation?” 58 Proc. Royal Soc’y Med. 295 (1965) [cited as Hill].

As a tribute to Hill, the publication was noteworthy, but Hill, if still alive, would be surprised, perhaps shocked, certainly annoyed, that the text of his Presidential Address was still being cited as the canonical statement of causal assessments more than half a century later. Epidemiologic science has progressed in many important ways since 1964, and science has refined and improved substantially upon Hill’s articulation of the epidemiologic method for assessing the causality of observed associations. Nonetheless, lawyers, on both sides of the bar, continue to publish analyses of Hill’s 1965 publication. A few years ago, Dr. Frank Woodside and Allison Davis published one such article.1 This month, a lawyer from the lawsuit industry published a plaintiffs’ vision of Hill’s methodology, in Trial, the trade journal of his industry. R. Jason Richards, “Reflecting on Hill’s Original Causation Factors,” 52 Trial 44 (Nov. 2016) [cited as Richards]Frankly, everyone would be better off if they simply read the original speech and understood it for what it was – a 50 year-old informal statement of a complex problem.

As do many defense lawyers, Richards treats the Hill factors as a canonical guide to causation. He states that “[t]he scientific community has generally accepted these viewpoints, and scientists regularly use them to assess causality between exposure and an outcome,” and then cites legal decisions only. Richards at 45.2

Some of Richards’ exegesis is a benign, helpful reminder to consider Hill’s recommendations in their original context, and that no one factor is typically dispositive. Richards at 49. What Richards fails to say is that a concordance of factors will often be needed to establish causation, as they were in showing that smoking caused lung cancer.

Richards tells us, plaintively and accurately, that “[m]any of Hill’s key insights about how to make decisions based on epidemiological evidence have been largely ignored or distorted over the years.” Richards at 46. Richards has in mind the indeterminacy of any given factor, which he casts as “no single interpretation is infallible.” Of course, if no single interpretation is infallible, then all are fallible, which Richards no doubts sees as immunizing any expert witness’s opinion against exclusionary gatekeeping.

Ultimately, Richards becomes yet another author who abridges and bastardizes Hill’s key insights. He further urges his readers to consider and invoke “seldom-cited but significant passages” from Hill (1965). One passage that Richards fails to consider and invoke himself is Hill’s important predicate for considering the nine factors in the first place. The starting point for Hill, as he clearly expressed in his President’s address, was that

[o]ur 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?”

Hill at 295. The nine factors follow, with elaboration, but importantly, the nine factors answer the question Hill posed about the aspects of the association, which we have already seen to be “perfectly clear-cut and beyond what we care to attribute to the play of chance.” To be sure, and fair, Hill did not use the words valid and statistically significant, but his meaning was perfectly clear and is completely ignored by Richards and occasionally by trial courts led into error by lawyers who advocate for causal associations on weak, inconsistent evidence from invalid associations.

There is some mischief perpetuated by treating Hill’s language as legislating a decision procedure for demonstrating causality. First, Hill spoke informally, without the scholarly apparatus of footnotes or extensive research. So when Hill wrote that we should not dismiss a putative causal claim merely because the association is “slight,” he was writing in the context of known causal associations with relative risks in the 100s (for chimney sweeps and scrotal cancer) or twenty to thirty fold for smoking and lung cancer. A slight association might well be one that is a merely doubling or tripling of base rate for the outcome. And Hill was not urging that such slight associations were much in the way of evidence for causality, only that we should not dismiss the causal claim solely because of the small size of the association.

Second, some of what Hill said was wrong when he said it in 1965. For instance, he wrote that none of the factors is necessary. The temporality factor, which specifies that the putative cause come before the putative effect, however, is indeed necessary. Unless of course we have “spooky action at a distance” that permits simultaneous causality across the universe in biomedicine. Hill was probably not thinking about the philosophical problems invoked by quantum physics when he incorrectly branded temporality as not necessary.

Third, some of what Hill said is distorted in commentary, such as Richards’, by ignorance, or by design. Perhaps the distortions occur because Hill was speaking colloquially to fellow scientists. Much of what he said, and is written in his 1965 article, is nuanced and contextual. For instance, Hill wrote that:

[n]o 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.”

Hill at 299 (emphasis added). This passage, referenced by Richards, is a favorite of lawsuit industry lawyers but an astute reader will note that it opens with a reference to “those questions.” To understand what “those questions” are, one would sensibly look at the paragraph above. In that previous paragraph, Hill states that the “proof” of causality requires judgment, and that the nine factors cannot be taken as providing a quod erat demonstrandum; rather the factors structure the inquiry, which is essential, into how to explain the facts observed and to rule out explanations that do not turn on causality. Understandably, statistical analyses do not replace the judgment involved in synthesizing the evidence with respect to the available factors. Of course, formal tests of significance do answer specific questions, such as whether the association in one beyond that which we care to ascribe to chance, a consideration that must be made before delving into the nine factors. And even after crossing the threshold of statistically significant, valid association, an analyst would consider statistical tests in connection with consistency and exposure gradient. The former consideration today is often addressed by meta-analyses, tests of homogeneity, and p-curves, none of which was in common use in 1965, at the time of Hill’s Presidential address. Some simple tests for exposure gradient were available, but often the judgment was left to a visual assessment of the dose-response curve. Today, formal statistical analyses would indeed be invoked to determine whether the dose-curve was likely inconsistent with random variability.

Fourth, what Hill said in 1965 must be viewed in the light of over 50 years of scientific developments in field of epidemiology and statistics. Ultimately, what Richards presents as précis is a gross distortion of Hill in 1965, and and an even greater distortion of acceptable scientific method in 2016.

Just as it is helpful to study historical propaganda, Richards’ article should be studied to understand how the lawsuit industry will try to induce error in judicial judgments. Richards argues that all the nine factors are “inherently subjective,” and so it is understandable and inevitable that expert witnesses will disagree. Richards at 48. The fact is that some of the Hill factors are not the least subjective. Strength, consistency, and exposure gradient, for instance, are all objective, quantifiable variables that can be used to evaluate a body of available epidemiologic studies (after the the available studies have shown associations perfectly clear cut and beyond the play of chance).

Richards deploys a sophistical argument that because none of the factors is necessary, then causation can be inferred in the absence of any of the factors, or perhaps in the presence of only the most unimportant factors, such as analogy. Readers will be hard pressed to come up with an example of a generally accepted causal relationship evidenced solely by an analogy or by a subjective assessment of plausibility, but Richards argues for the validity of such an inference in the abstract, and in the absence of any real-world examples.

To some lawyers, all facts are created equal.” Felix Frankfurter3

To some lawyers, all epidemiologic studies are created equal, but they too are mistaken. Richard attempts to engage in extreme deconstuctionist analysis, verging on Daubert by Derrida. Richards avers that all studies are flawed, and one can always find always question a study’s validity, and “so it would be a stretch to establish any cause-and-effect relationship if statistically significant data were the only acceptable basis for asserting causation.” Richards at 48. Richards confuses validity with statistical significance, but worse, his argument ignores important qualitative and quantitative differences between and among studies in terms of their design, implementation, analysis, and interpretation. His argument is akin to saying that all human beings have flaws so we should do away with honors and awards, as well as prisons and penalties.

Finally, no article such as Richards’ would be complete without misstating the holding and dictum in Matrixx Initiatives:

The U.S. Supreme Court has finally recognized this as well, agreeing that statistical significance is not the touchstone of reliability under a Daubert analysis.”

Richards at 48 (citing Matrixx Initiatives, Inc. v. Siracusano, 131 S. Ct. 1309, 1319 (2011)). This is a remarkably misleading citation given that the Court, in Matrixx, noted that it was not considering whether “expert testimony was properly admitted,” and that it was not trying “to define here what constitutes reliable evidence of causation.” Matrixx, 131 S. Ct. at 1319.

In reaching, nay stretching, to address the statistical issue, the Supreme Court cited three cases, two of which involved differential etiology and contested specific causation, for which statistical analysis was absent and irrelevant. Id. (citing Best v. Lowe’s Home Centers, Inc., 563 F.3d 171, 178 (C.A.6 2009); Westberry v. Gislaved Gummi AB, 178 F.3d 257, 263-264 (C.A.4 1999)). The third case was relevant to statistical inference, but involved a case in which the plaintiff’s expert witness had cited at least one study with a nominally statistically significant result, which was vitiated by internal and external validity concerns. Wells v. Ortho Pharmaceutical Corp., 788 F.2d 741, 744-745 (11th 1986), cert. denied, 479 U.S.950 (1986).4

Richard asserts that “Hill recognized that scientists could efficiently use data and draw reasonable inferences about causation in the absence of statistically significant findings.” Richards at 48. Tellingly, Richards provides no citation for his assertion. There is none, but nothing is the hallmark of epistemic nihilism.

1 Frank C. Woodside, III & Allison G. Davis, “The Bradford Hill Criteria: The Forgotten Predicate,” 35 Thomas Jefferson L. Rev. 103 (2013).

2 citing In re Trasylol Prods. Liab. Litig., 2010 WL 1489734, at *8-9 (S.D. Fla. Mar. 8, 2010); In re Zoloft (Sertraline Hydrochloride) Prods. Liab. Litig., 26 F. Supp. 3d 449, 454 (E.D. Pa. 2014).

3 quoted in Comes v. Microsoft Corp., 709 N.W.2d 114, 116 (Iowa 2006) without source information.

Statistical Analysis Requires an Expert Witness with Statistical Expertise

November 13th, 2016

Christina K. Connearne sued her employer, Main Line Hospitals, for age discrimination. Main Line charged Connearne with fabricating medical records, but Connearne replied that the charge was merely a pretext. Connearney v. Main Line Hospitals, Inc., Civ. Action No. 15-02730, 2016 WL 6569292 (E.D. Pa. Nov. 4, 2016) [cited as Connearney]. Connearne’s legal counsel engaged Christopher Wright, an expert witness on “human resources,” for a variety of opinions, most of which were not relevant to the action. Alas, for Ms. Connearne, the few relevant opinions proffered by Wright were unreliable. On a Rule 702 motion, Judge Pappert excluded Wright from testifying at trial.

Although not a statistician, Wright sought to offer his statistical analysis in support of the age discrimination claim. Connearney at *4. According to Judge Pappert’s opinion, Wright had taken just two classes in statistics, but perhaps His Honor meant two courses. (Wright Dep., at 10:3–4.) If the latter, then Wright had more statistical training than most physicians who are often permitted to give bogus statistical opinions in health effects litigation. In 2015, the Medical College Admission Test apparently started to include some very basic questions on statistical concepts. Some medical schools now require an undergraduate course in statistics. See Harvard Medical School Requirements for Admission (2016). Most medical schools, however, still do not require statistical training for their entering students. See Veritas Prep, “How to Select Undergraduate Premed Coursework” (Dec. 5, 2011); “Georgetown College Course Requirements for Medical School” (2016).

Regardless of formal training, or lack thereof, Christopher Wright demonstrated a profound ignorance of, and disregard for, statistical concepts. (Wright Dep., at 10:15–12:10; 28:6–14.) Wright was shown to be the wrong expert witness for the job by his inability to define statistical significance. When asked what he understood to be a “statistically significant sample,” Wright gave a meaningless, incoherent answer:

I think it depends on the environment that you’re analyzing. If you look at things like political polls, you and I wouldn’t necessarily say that serving [sic] 1 percent of a population is a statistically significant sample, yet it is the methodology that’s used in the political polls. In the HR field, you tend to not limit yourself to statistical sampling because you then would miss outliers. So, most HR statistical work tends to be let’s look at the entire population of whatever it is we’re looking at and go from there.”

Connearney at *5 (Wright Dep., at 10:15–11:7). When questioned again, more specifically on the meaning of statistical significance, Wright demonstrated his complete ignorance of the subject:

Q: And do you recall the testimony it’s generally around 85 to 90 employees at any given time, the ER [emergency room]?

A: I don’t recall that specific number, no.

Q: And four employees out of 85 or 90 is about what, 5 or 6 percent?

A: I’m agreeing with your math, yes.

Q: Is that a statistically significant sample?

A: In the HR [human resources] field it sure is, yes.

Q: Based on what?

A: Well, if one employee had been hit, physically struck, by their boss, that’s less than 5 percent. That’s statistically significant.”

Connearney at *5 n.5 (Wright Dep., at 28:6–14)

In support of his opinion about “disparate treatment,” Wright’s report contained nothing than a naked comparison of two raw percentages and a causal conclusion, without any statistical analysis. Even for this simplistic comparison of rates, Wright failed to explain how he obtained the percentages in a way that permitted the parties and the trial court to understand his computation and his comparisons. Without a statistical analysis, the trial court concluded that Wright had failed to show that the disparity in termination rates among younger and older employees was not likely consistent with random chance. See also Moultrie v. Martin, 690 F. 2d 1078 (4th Cir. 1982) (rejecting writ of habeas corpus when petitioner failed to support claim of grand jury race discrimination with anything other than the numbers of white and black grand jurors).

Although Wright gave the wrong definition of statistical significance, the trial court relied upon judges of the Third Circuit who also did not get the definition quite right. The trial court cited a 2010 case in the Circuit, which conflated substantive and statistical significance and then gave a questionable definition of statistical significance:

The Supreme Court has not provided any definitive guidance about when statistical evidence is sufficiently substantial, but a leading treatise notes that ‘[t]he most widely used means of showing that an observed disparity in outcomes is sufficiently substantial to satisfy the plaintiff’s burden of proving adverse impact is to show that the disparity is sufficiently large that it is highly unlikely to have occurred at random.’ This is typically done by the use of tests of statistical significance, which determine the probability of the observed disparity obtaining by chance.”

See Connearney at *6 & n.7, citing and quoting from Stagi v. National RR Passenger Corp., 391 Fed. Appx. 133, 137 (3d Cir. 2010) (emphasis added) (internal citation omitted). Ultimately, however, this was all harmless error on the way to the right result.

Benhaim v. St. Germain – Supreme Court of Canada Wrestles With Probability

November 11th, 2016

On November 10, 2016, the Supreme Court of Canada handed down a divided (four-to-three decision) in a medical malpractice case, which involved statistical evidence, or rather probabilistic inference. Benhaim v. St-Germain, 2016 SCC 48 (Nov. 10, 2016).  The case involved an appeal from a Quebec trial court, and the Quebec Court of Appeal, and some issues peculiar to Canadian lawyers. For one thing, Canadian law does not appear to follow lost-chance doctrine outlined in the American Law Institute’s Restatement. The consequence seems to be that negligent omissions in the professional liability context are assessed for their causal effect by the Canadian “balance of probabilities” standard.

The facts were reasonably clear, although their interpretation were disputed. In November 2005, Mr. Émond was 44 years old, a lifelong non-smoker, and in good health. At his annual physical with general practitioner Dr. Albert Benhaim, Émond had a chest X-ray (CXR). Benhaim at 11, 6. Remarkably, neither the majority nor the dissent commented upon the lack of reasonable medical necessity for a CXR in a healthy, non-smoking 40-something male. Few insurers in the United States would have paid for such a procedure. Maybe Canadian healthcare is more expansive than what we see in the United States.

The radiologist reviewing Mr. Émond’s CXR reported a 1.5 to 2.0 cm solitary lesion, and suggested a review with previous CXRs and a recommendation for a CT scan of the thorax. Dr. Benhaim did not follow the radiologist’s suggestions, but Mr. Émond did have a repeat CXR two months later, on January 17, 2006, which was interpreted as unchanged. A recommendation for a follow-up third CXR in four months was not acted upon. Benhaim at 11, 7. The trial court found that the defendant physicians deviated from the professional standard of care, a finding from which there was no appeal.

Mr. Émond did have a follow-up CXR at the end of 2006, on December 4, 2006, which showed that the solitary lung nodule had grown. Follow up CT and PET scans confirmed that Mr. Émond had Stage IV lung cancer. Id.

The issues in controversy turned on the staging of Mr. Émond’s lung cancer at the time of his first CXR, in November 2005, the medical consequences of the delay in diagnosis. Plaintiffs presented expert witness opinion testimony that Mr. Émond’s lung cancer was only Stage I (or at most IIA), at initial radiographic discovery of a nodule, and that he was at Stage III or IV in December 2006, when CT and PET scans confirmed the actual diagnosis of lung cancer. In the view of plaintiff’s expert witnesses, the delay in diagnosis, and the accompanying growth of the tumor and change from Stage I to IV, dramatically decreased Émond’s chance of survival. Id. At 13, 15-16. Indeed, plaintiff’s expert witnesses opined that had Mr. Émond been timely diagnosed and treated in November 2005, he probably would have been cured.

The defense expert witness, Dr. Ferraro, testified that Mr. Émond’s lung cancer was Stage III or IV in November 2005, when the radiographic nodule was first seen, and his chances of survival at that time were already quite poor. According to Dr. Ferraro, earlier intervention and treatment would probably not have been successful in curing Mr. Émond, and the delay in diagnosis was not a cause of his death.

The trial court rejected plaintiffs’ expert witnesses’ opinions on factual grounds. These witnesses had argued that Mr. Émond’s lung cancer was at Stage I in November 2005 because the lung nodule was less than 3 cm., and because Mr. Émond was asymptomatic and in good health. These three points of contention were clearly unreliable because they were all present in January 2007, when Mr. Émond was diagnosed with Stage IV cancer, according to all the expert witnesses. Every point cited by plaintiffs’ expert witnesses in support of their staging failed to discriminate Stage I from Stage III. In Her Honor’s opinion, the lung cancer was probably Stage III in November 2005, and this staging implied a poor prognosis on all the expert witnesses’ opinions. The failure to diagnose until late 2006 was thus not, on the “balance of probabilities” a cause of death. Id. At 15, ¶21.

The intermediate appellate court reversed on grounds of a presumption of causation, which comes into being when the defendant’s negligence interferes with plaintiff’s ability to show causation, and there is some independent evidence of causation to support the case. I will leave this presumption, which the Supreme Court of Canada held inappropriate on the facts of this case, to Canadian lawyers to debate. What was more interesting was the independent evidence adduced by plaintiffs. This evidence consisted of statistical evidence in the form of generality that 78 percent of fortuitously discovered lung cancers are at Stage I, which in turn is associated with a cure rate of 70 percent. Id. at 18 30.

The plaintiffs’ witnesses hoped to apply this generality to this case, notwithstanding that Émond’s nodule was close to 2 cm. on CXR, that the general statistic was based up more sensitive CT studies, and that Émond had been a non-smoker (which may have influenced tumor growth and staging). Furthermore, there was an additional, ominous finding in Mr. Émond’s first CXR, of hilar prominence, which supported the defense’s differentiation of his case from the generality of fortuitously discovered (presumably small, solitary lung nodules without hilar involvement). Id. at 44 83.

The trial court rejected the inference from the group statistic of 70% survival to the conclusion that Mr. Émond had a 70% probability of survival. Tellingly, there was no discussion of the variance for the 70% figure; nor any mention of relevant subgroups. The Court of Appeals, however, would have turned this statistic into a binding presumption by virtue of accepting the 78 percent as providing strong evidencec that the 70% survival figure pertained to Mr. Émond. The intermediate appellate court would then have taken the group survival rate as providing a more likely than not conclusion about Mr. Émond, while rejecting the defense expert witness’s statistics as mere speculation. Id. at 36 ¶67.

Adopting a skeptical stance with respect to probabilistic evidence, the Supreme Court reversed the Quebec Court of Appeal’s reversal of the trial court’s judgment. The Court cited Richard Wright and Jonathan Cohen’s criticisms of probabilistic evidence (and Cohen’s Gatecrasher’s Paradox), and urged caution in applying class or group statistics to generate probabilities that class members share the group characteristic.

Appellate courts should generally not interfere with a trial judge’s decision not to draw an inference from a general statistic to a particular case. Statistics themselves are silent about whether the particular parties before the court would have conformed to the trend or been an exception from it. Without an evidentiary bridge to the specific circumstances of the plaintiff, statistical evidence is of little assistance. For this reason, such general trends are not determinative in particular cases. What inferences follow from such evidence — whether the generalization that a statistic represents is instantiated in the particular case — is a matter for the trier of fact. This determination must be made with reference to the whole of the evidence.”

Benhaim at 39, 74, 75 (internal citations omitted).

To some extent, the Supreme Court’s comments about statistical evidence were rather wide of there mark. The 78% statistic was based upon a high level of generality, namely all cases, without regard for the size of the radiographically discovered lesion, the manner of discovery (CXR versus CT), presence or absence of hilar pathology, or group or individual’s smoking status. In the context of the facts of the case, however, the trial court clearly had a factual basis for resisting the application of the group statistic (78% fortuitously discovered tumors were Stage I with 70% five-year survival).

The Canadian Supreme Court seems to have navigated these probabilistic waters fairly adeptly, although the majority opinion contains broad brush generalities and inaccuracies, which will, no doubt, show up in future lower court cases. For instance:

This is because the law requires proof of causation only on a balance of probabilities, whereas scientific or medical experts often require a higher degree of certainty before drawing conclusions on causation (p. 330). Simply put, scientific causation and factual causation for legal purposes are two different things.”

Benhaim at 24, 47. The Court cited legal precedent for its observation, and not any scientific treatises. And then, the Supreme Court suggested that all one needs to prevail in a tort case in Canada is a medical expert witness who speculates:

Trial judges are empowered to make legal determinations even where medical experts are not able to express an opinion with certainty.

Benhaim at 37, 72Clearly dictum on the facts of Benhaim, but it seems that judges in Canada are like those in the United States. Black robes empower them to do what mere scientists could not do. If we were to ignore the holding of Benhaim, we might think that all one needs in Canada is a medical expert who speculates.

Talc Litigation – Stop the Madness

November 10th, 2016

Back in September, Judge Johnson, of New Jersey, wrapped up a talc ovarian cancer case in Kemp, and politely excused the case from any further obligations to show up in court. Carl v. Johnson & Johnson, No. ATL-L-6546-14, 2016 WL 4580145 (N.J. Super. Ct. Law Div., Atl. Cty., Sept. 2, 2016) [cited as Carl]. See “New Jersey Kemps Ovarian Cancer – Talc Cases” (Sept. 16, 2016).

In Giannecchini v. Johnson & Johnson, a Missouri jury returned a substantial verdict for plaintiff. The jury, by a 9 to 3 vote, awarded $575,000 for claimed economic loss, and $2 million for non-economic compensatory damages. The jury also found defendant Johnson & Johnson in need of punishment to the tune of $65,000,000, and Imerys Talc America Inc. for $2.5 million. Plaintiffs, having sought $285 million, were no doubt disappointed. The Giannecchini verdict was the third large verdict in the Missouri talc litigation. See Myron Levin, “Johnson & Johnson Hammered Again in Talc-Ovarian Cancer Verdict of $70 Million,” (Oct. 27, 2016); Brandon Lowrey, “J & J, Talc Co. Hit With $70M Baby Powder Cancer Verdict,” Law360 (Oct. 2016).

In his closing argument, Giannecchini’s lawyer, R. Allen Smith, reportedly accused Johnson & Johnson of having “rigged” regulatory agencies to ignore the dangers of talc, and of having “falsified” medical records to hide the problem. Smith implored the jury to “make them stop”; make them “stop this madness.”

Make them stop the madness, indeed. The November 2016 issue of Epidemiology features a publication of the “Sister Study,” which explored whether there was any association between perineal talc use and ovarian cancer. The authors acknowledged, as had Judge Johnson in the Carl case, that some prior case-control studies had found an increased risk of ovarian cancer, but that prospective cohort studies have not confirmed an association. Nicole L. Gonzalez, Katie M. O’Brien, Aimee A. D’Aloisio, Dale P. Sandler, and Clarice R. Weinberg, “Douching, Talc Use, and Risk of Ovarian Cancer,” 27 Epidemiology 797 (2016).

The Sister Study (2003–2009) followed a cohort of 50,884 women whose sisters had been diagnosed with breast cancer. Talc use was ascertained at baseline, before diagnosis of subsequent disease and before any chance for selective recall. The cohort was followed for a median of 6.6 years, in which time there were 154 cases of ovarian cancer during the follow up, available for analysis using Cox’s proportional hazards model. Perineal talc use at baseline was not associated with later ovarian cancer. The authors reported a hazard ratio of 0.73, less than expected, with a 95% confidence interval of 0.44, 1.2.

So, yes, make them stop this madness; close the gate.

The opinions, statements, and asseverations expressed on Tortini are my own, or those of invited guests, and these writings do not necessarily represent the views of clients, friends, or family, even when supported by good and sufficient reason.