TORTINI

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

Contrivance Standard Applied to Gatekeepers and Expert Witnesses

October 1st, 2014

In Rink v. Cheminova, Inc., 400 F.3d 1286 (11th Cir. 2005), the Eleventh Circuit’s articulated a “contrivance standard,” which suggested that a district court “may properly consider whether the expert’s methodology has been contrived to reach a particular result.” Id. at 1293 & n.7; see alsoThe Contrivance Standard for Expert Witness Gatekeeping” (Sept. 28, 2014).

Although this standard has some appeal, it raises questions of motives that can complicate the Rule 702 inquiry into whether an purported opinion is “knowledge.” A less psychoanalytic inquiry into the expert witness’s motivation should generally be the first line of approach.

In the Zoloft MDL, the trial court banished Dr. Anick Bérard from federal court birth defect cases because of her unprincipled and inexplicable cherry picking of data, relied upon for her causation opinions. See In re Zoloft (Sertraline Hydrochloride) Prods. Liab. Litig. MDL No. 2342; 12-md-2342, 2014 U.S. Dist. LEXIS 87592; 2014 WL 2921648 (E.D. Pa. June 27, 2014) (Rufe, J.). The “contrivance” was objectively obvious and manifest in double-counting data points, and ignoring point estimates that were contrary to the desired outcome, even from papers that provided point estimates that were selectively embraced.

In the Chantix MDL, the trial court found the defendant to have harped on methodological peccadilloes but obviously did not like the beatific music (3x). Cherry picking was going on, but it was perfectly acceptable to this MDL court:

“Why Dr. Kramer chose to include or exclude data from specific clinical trials is a matter for crossexamination, not exclusion under Daubert.

In re Chantix (varenicline) Prods. Liab. Litig., 889 F. Supp. 2d 1272, 1288 (2012) (MDL No. 2092) (permitting Dr. Shira Kramer to testify on causation despite her embracing a “weight of the evidence” method that turned largely on‘‘subjective interpretations’’ of various, undescribed, non-prespecified lines of evidence).

The differing approaches to cherry picking are hard to reconcile other than to note that Chantix had drawn a “black box” warning from the FDA, and the SSRIs involved in Zoloft had not been given any heightened warning from the FDA, foreign agencies, or any professional society. FDA labeling, of course, should not have been determinative of the causation question. The mind of the gatekeeper, however, is inscrutable.

 

 

The Contrivance Standard for Expert Witness Gatekeeping

September 28th, 2014

According to Google ngram, the phrase “junk science” made its debut circa 1975, lagging junk food by about five years. SeeThe Rise and Rise of Junk Science” (Mar. 8, 2014). I have never much like the phrase “junk science” because it suggests that courts need only be wary of the absurd and ridiculous in their gatekeeping function. Some expert witness opinions are, in fact, serious scientific contributions, just not worthy of being advanced as scientific conclusions. Perhaps better than “junk” would be patho-epistemologic opinions, or maybe even wissenschmutz, but even these terms might obscure that the opinion that needs to be excluded derives from serious scientific, only it is not ready to be held forth as a scientific conclusion that can be colorably called knowledge.

Another formulation of my term, patho-epistemology, is the Eleventh Circuit’s lovely “Contrivance Standard.” Rink v. Cheminova, Inc., 400 F.3d 1286, 1293 & n.7 (11th Cir. 2005). In Rink, the appellate court held that the district court had acted within its discretion to exclude expert witness testimony because it had properly confined its focus to the challenged expert witness’s methodology, not his credibility:

“In evaluating the reliability of an expert’s method, however, a district court may properly consider whether the expert’s methodology has been contrived to reach a particular result. See Joiner, 522 U.S. at 146, 118 S.Ct. at 519 (affirming exclusion of testimony where the methodology was called into question because an “analytical gap” existed “between the data and the opinion proffered”); see also Elcock v. Kmart Corp., 233 F.3d 734, 748 (3d Cir. 2000) (questioning the methodology of an expert because his “novel synthesis” of two accepted methodologies allowed the expert to ”offer a subjective judgment … in the guise of a reliable expert opinion”).”

Note the resistance, however, to the Supreme Court’s mandate of gatekeeping. District courts must apply the statutes, Rule of Evidence 702 and 703. There is no legal authority for the suggestion that a district court “may properly consider wither the expert’s methodology has been contrived.” Rink, 400 F.3d at 1293 n.7 (emphasis added).

Examining Expert Witnesses Before Trial – Getting Personal

September 27th, 2014

Personal and cognitive biases are major issues in challenging expert witnesses and their opinions. Discovery is an important opportunity to explore substantive issues, but some time should be allocated to inquiring about biases. Unfortunately, many lawyers inquire about fees and income and stop. At the end of a case, the jury will have heard that all the expert witnesses, typically, are charging for their time, and the jury’s initial shock at exorbitant fees will subside. Finding more revealing biases than income should be one of the goals of a pre-trial deposition.

One question that I try always to ask of expert witnesses is whether they have any friends or family members who have been injured by a product, and especially my client’s product. You never know until you ask.

Here is how the inquiry went with one expert witness in the field of history:

Q. Has anyone in your family or any close friend ever in your belief been injured by a product?

A. Well, this would only be my own belief. I don’t know that this is true. I have no specific knowledge of it.

Q. Sure.

A. It was never brought to court, but I believe my father was.

Q. In what way?

A. Well, he always had a very bad cough and he had always been very — he had been exposed as a worker in many different conditions to various dusts.

Q. What kind of dusts?

A. I have no idea. He worked in a foundry. He worked in a steel mill.

Deposition Transcript at 32:7-23, taken in Mendez v. American Optical, 342d Judicial District, District Court of Tarrant County, Texas (July 13, 2005)

In insurance coverage cases, I have asked defense expert witnesses whether they have advised family members against using products, the safety of which was at issue. Again, on more than one occasion, I have elicited testimony that family members were using the product and had no ill effects. In each case, the expert witness for the defendants withdrew rather than testify at trial about why they permitted a close relative to use the product, which they had maligned in their litigation opinion. Here is the Q&A in a deposition of one frequent testifier:

Q. By the way, has anyone in your family or any of your friends ever been implanted with a silicone medical device?

A. Yes.

Q. And does that have any significance in your reaching your opinions?

A. No.

Q. Is it a friend or a family member?

A. Family member.

Q. In your view, did that family member sustain any harm as a result of the silicone implant?

A. I have no comment to make about that. There have been no complaints and no difficulties. So, so far, I can’t answer the question. I’m not her physician.

Q. Does that person have a legal suit involving the silicone medical device?

A. No.

Deposition transcript at 19-20, in Claus v. Cooper Surgical, Inc., California Superior Court for San Diego County, JCCP-2754-00243, and Santa Clara County, No. 922061 (Dec. 6, 1994). The “cold” record does not capture the witness’s discomfort. The deposition was not concluded, and the witness withdrew rather than continue with his advocacy. See also Deposition transcript in Medical Engineering v AIU Insurance, 58th Judicial District, District Court for Jefferson County, Texas (Feb. 6, and 7, 1997).

Moving beyond the obvious financial incentives for expert witnesses, there are many other sources of potential and actual bias. Injuries and diseases among family members and friends are just the beginning. Memberships in advocacy groups, political organizations, and special-interest professional associations are other issues to be discovered and explored. Many expert witnesses have signed on to amicus briefs that have taken tendentious positions in high-profile cases. Beware of advocate expert witness opinion testimony.

 

 

Maryland Refuses Apportionment in Asbestos Lung Cancer Cases – Carter

September 19th, 2014

In Carter v. The Wallace & Gale Asbestos Settlement Trust, 439 Md. 333, 96 A.3d 147 (2014), the Maryland Court of Appeals missed an opportunity to place causal apportionment of damages in asbestos cases on a sound legal and factual basis. Instead, the Court misinterpreted the law to be about fault instead of causation, and it failed to come to terms with the facts that supported apportionment.

Carter was a consolidation of four lung cancer cases for trial before a single jury. All plaintiffs had substantial smoking histories, with varying degrees of asbestos exposure. None of the plaintiffs had been an insulator or worked in an asbestos factory. In one of the cases, involving Roger C. Hewitt, Sr., defendant Wallace & Gale Asbestos Settlement Trust[1] proffered a report of its expert witness, Dr. Gerald R. Kerby, who opined that the Mr. Hewitt’s lung cancer and death was apportionable, 3:1, between two causes, smoking and asbestos. 96 A.3d at 151-52.

The plaintiffs’ expert witness, Dr. Steven Zimmet provides the catechistic testimony, based upon the Mt. Sinai scriptures. Zimmet testified that “he could not differentiate between the two causes because the two exposures [asbestos and tobacco] are ‛not just additive, they are synergistic which means they multiple exposures’.” Id. at 151. Of course, Zimmet’s profession of ignorance was hardly probative of whether an apportionment could be made. The distinction, however, between knowledge that something cannot be done, and ignorance as to how it might be done, was lost upon the trial judge, who was wildly dismissive of the proffered opinion from Dr. Kerby:

“No, I understand there is a statistical basis for likelihood of risk. But in a given—with a given plaintiff, I don’t know how you can apportion it. But, you know, I guess, the witness can say what he says if he is qualified to say it. But I’m not going to give an instruction on this because it is not — I don’t perceive it at this point to be the law in these types of cases.

* * *

You can apportion risk. I don’t know how, in an individual plaintiff[‘s] case, you can apportion damages. I don’t know. It is a mystery to me. We’ll find out. The doctor will show up and we will hear about it.”

Id. at 151.

The trial judge excluded Dr. Kerby’s apportionment opinion, based upon a filed offer of proof, and refused to charge the jury on apportionment of damages. As for the jury instruction on apportionment, the trial judge ventured that the defendant was asking to the jury to make “a very unscientific wild guess.” Id. at 151. Of course, allowing the jury to decide any causation claim upon evidence of increased risk sanctions wild guesses and unscientific speculation. Risk is not causation. See, e.g., Guinn v. AstraZeneca Pharms., 602 F.3d 1245, 1255 (11th Cir. 2010) (“An expert, however, cannot merely conclude that all risk factors for a disease are substantial contributing factors in its development. ‘The fact that exposure to [a substance] may be a risk factor for [a disease] does not make it an actual cause simply because [the disease] developed.’”) (internal citation omitted). See also Richard Doll, “Proof of Causality: Deduction from Epidemiological Observation,” 45 Perspectives in Biology & Medicine 499, 500 (2002) (“That asbestos is a cause of lung cancer in this practical sense is incontrovertible, but we can never say that asbestos was responsible for the production of the disease in a particular patient, as there are many other etiologically significant agents to which the individual may have been exposed, and we can speak only of the extent to which the risk of the disease was increased by the extent of his or her exposure.”). Given that courts have put juries into the business of making wild guesses, the trial court failed to explain why it could not make a guess based upon the same sort of increased risk evidence that would support a finding of causation against the asbestos defendant alone.

The jury returned verdicts for all four plaintiffs, and the defendant appealed. The Maryland Special Court of Appeals reversed and remanded the Hewitt case for a new trial.[2] Wallace & Gale Trust v. Carter, 65 A.3d 749, 752 (Md. App. 2013). The Maryland Court of Appeals, however, took the plaintiff’s appeal, and reinstated the verdict in favor of the Hewitt family[3].

The Court of Appeals did not fuss over the general statement of Maryland law of apportionment of damages, which has adopted the American Law Institute’s Restatement (Second) of Torts § 433A (1965), which provides:

“(1) Damages for harm are to be apportioned among two or more causes where

          (a) there are distinct harms, or

(b) there is a reasonable basis for determining the contribution of each  cause to a single harm.

(2) Damages for any other harm cannot be apportioned among two or more causes.”

Id. at 157-58, quoting the Restatement. The Court did not explain why it was relying upon a portion of the Restatement, which has been superseded by the Restatement Third of Torts: Apportionment of Liability § 26 (2000).

In any event, the Court of Appeals did recognize that the crucial issue was whether there was a reasonable basis for determining the contribution of each cause to a single harm. On this issue, the Carter court took its lead from antiquated dicta from a treatise, 30 years out of date. W. Page Keeton, et al., Prosser and Keeton on Torts § 52, at 345 (5th ed. 1984). See Georgetown Law Library, “Torts Law Treatises” (“This classic hornbook on torts is no longer up-to-date… .”). The Court quoted:

“The distinction is one between injuries which are reasonably capable of being separated and injuries which are not. If two defendants, struggling for a single gun, succeed in shooting the plaintiff, there is no reasonable basis for dividing the injury between them, and each will be liable for all of it. If they shoot the plaintiff independently, with separate guns, and the plaintiff dies from the effect of both wounds, there can still be no division, for death cannot be divided or apportioned except by an arbitrary rule devised for that purpose. If they merely inflict separate wounds, and the plaintiff survives, a basis for division exists, because it is possible to regard the two wounds as separate injuries; and the same of course is true for wounds negligently inflicted…. Upon the same basis, if two defendants each pollute a stream with oil, in some instances it may be possible to say that each has interfered to a separate extent with the plaintiff’s rights in the water, and to make some division of the damages. It is not possible if the oil is ignited, and burns the plaintiff’s barn.”

96 A.3d at 158, quoting Prosser and Keeton on Torts § 52, at 345-47 (5th ed. 1984) (internal citations omitted). As can be seen from the language quoted by Court, the venerable, but out-dated text never even considered an apportionment of an injury where the only information about causation was the existence of ex ante risks. Conspicuously absent from the hornbook are any examples of cases in which causation itself is predicated upon quantitative risk estimates, which in turn could readily supply the basis for apportionment.

As for the science, the Court of Appeals cited a textbook written by plaintiffs’ lawyers:

“asbestos and tobacco smoke are complex carcinogens that can affect multiple steps in the multistage process of cancer evolution, and that the combined effects will depend on the relative magnitude of each carcinogen at each stage. As reported in different studies, the interactive effect ranges from less than additive to supramultiplicative [sic] but the model for insulation workers approximates a multiplicative effect. If the multistage model of carcinogenesis holds, and asbestos and smoking act at different stages, then a multiplicative relationship follows.”

96 A.3d at 160-61, quoting from George A. Peters & Barbara J. Peters, Asbestos Pathogenesis and Litigation, 13 The Sourcebook on Asbestos Diseases: Medical, Legal, and Technical Aspects 149 (1996). Peters and Peters is a consulting and law firm in Santa Monica. Barbara J. Peters is a lawyer and a member of the Consumer Attorneys Association of Los Angeles, the Consumer Attorneys Association of California, and the Association of Trial Lawyers of America.

If the Court of Appeals had even bothered to read the plaintiffs’ lawyer tract, it would have seen that even the Peters had qualified their opinion, in their 1996 book, by suggesting that the “model for insulation workers approximates a multiplicative effect.” Id. (emphasis added). Mr. Hewitt had been a crane operator, which hardly involves the same level of exposure as an asbestos insulator, and the evidence for multiplicative synergy is sorely lacking outside a few, heavily exposed cohorts such as insulation workers. In any event, the Court of Appeals failed to explain or justify why a multiplicative model, even if it were appropriate, is decisive of the issue whether or not there was a reasonable basis for apportionment.

While we might excuse the Court of Appeals’ missteps in interpreting scientific evidence, even if filtered through funnels created by the plaintiffs’ expert witness Zimmet and the law firm of Peters & Peters, harder to forgive is the Court’s bobbling the interpretation of apportionment in New Jersey courts. The Special Court of Appeals had relied upon the New Jersey Dafler case, which affirmed a jury’s apportionment of damages in an asbestos and smoking lung cancer case. Dafler v. Raymark Industries, Inc., 259 N.J.Super. 17, 611 A.2d 136 (App. Div.1992), aff’d 132 N.J. 96, 622 A.2d 1305 (1993) (per curiam). In Dafler, the plaintiff’s expert witness made the usual protestations that the outcome, lung cancer, was indivisible, and the defense expert witness opined that smoking was the sole cause. The New Jersey appellate courts held that it would be manifestly unjust to attribute 100% of the lung cancer to smoking when no expert witness testified to such an allocation.

The Court of Appeals correctly pointed out that New Jersey cases are not binding upon it and that it would choose not to do so, which was its wont. The Court then proceeded to ignore that the Dafler holding was explicitly adopted by the New Jersey Supreme Court, and that the holding was based upon a causal, not a fault-based, apportionment. Indeed, the Court of Appeals went as far as to declare that the Dafler case was based upon fault principles because the Appellate Division there had stated that “apportionment is also consistent with the principles of the Comparative Negligence Act.” 96 A.3d at 155, quoting from Dafler, 259 N.J.Super. at 35, 611 A.2d at 145 (emphasis added). What the Maryland Court of Appeals failed to realize, however, was that the Dafler case was tried in New Jersey’s regime of hyper-strict asbestos liability, in which evidence of fault is excluded. Of necessity, the evidence and the verdict in Dafler were based exclusively upon causal determinants, not fault principles. Indeed, the Appellate Division’s “also” emphasized here in the quote from Dafler makes clear that the Appellate Division was merely noting that New Jersey juries are asked to make similar assessments of comparative contributions in fault, and that making such an assessment is not beyond the jury’s function or competence.

Two judges, in Carter, dissented in a polite, factual opinion that tore away at the majority opinion. The dissent noted that in Maryland, as in most states, workman’s compensation judges apportion causal shares to single injuries all the time. 96 A.3d at 173. And the dissent dug deeper into New Jersey law, as well as other foreign states, to expose the majority’s poor scholarship:

“Death may be indivisible as to result, but it is not per se incapable of apportionment. Many courts around the country have permitted apportionment in death cases. See e.g., Brisboy v. Fibreboard Corp., 429 Mich. 540, 418 N.W.2d 650, 655 (1988) (permitting apportionment of damages in a wrongful death action based on smoking history and asbestos exposure); Champagne v. Raybestos–Manhattan, Inc., 212 Conn. 509, 562 A.2d 1100, 1118 (1989) (same); see also Poliseno v. General Motors Corp., 328 N.J.Super. 41, 744 A.2d 679, 687 (2000) (concluding that while death is indivisible as to result, it is capable of apportionment in terms of causation). … In my view, a categorical rule that death is an indivisible injury incapable of apportionment speeds past an accepted principle of law: death can be capable of apportionment as to damages, but not as to fault. See Restatement (Third) of Torts: Physical and Emotional Harm § 28, cmt. d (2010) (“Death as an injury may not be divisible, but damages for death are divisible.”); see also Gerald W. Boston, Toxic Apportionment: A Causation and Risk Contribution Model, 25 Envtl. L. 549, 568–69 (1995) (stating that although “comment i [to the Restatement (Second) of Torts § 443A] states that death is the quintessential indivisible harm … deaths attributable to toxic causes, as when a plaintiff dies from lung cancer brought about by the combined effects of smoking and asbestos exposure, each of the contributing causes can be compared and the harm apportioned on that basis.”).

Id. at 173.

The dissent saw clearly that the characterization of apportionment in New Jersey law, relied upon by the intermediate appellate court, was not a mere matter of opinion. The majority of the Court of Appeals was wrong, as a matter of fact, in claiming that apportionment of damages in New Jersey was based upon fault. Id. at 174, citing Poliseno v. General Motors Corp., 328 N.J.Super. 41 55-56, 744 A.2d 679, 687-88 (2000), for clear distinguishing between apportionment based upon causation as opposed to fault.

The dissent also called out the majority for the disturbing partisanship in adopting plaintiffs’ lawyers’ and plaintiffs’expert witness’s opinions on apportionment, without any consideration of the excluded expert witness’s contrary opinions. See Gerald W. Boston, Toxic Apportionment: A Causation and Risk Contribution Model, 25 Envt’l L. 549, 555 (1995) (cited by dissenters for his conclusion that “[i]f the plaintiff’s asbestos exposure and his smoking are both shown to be causal factors in the plaintiff’s lung cancer, then the loss is necessarily capable of apportionment on the basis of the relative risks demonstrated for each kind of toxic exposure.”).

The Carter case comes about a year after the Court of Appeals reversed a careful opinion of the Special Court of Appeals, and held that plaintiffs’ expert witnesses may testify that each exposure, however small, represents a substantial contributing factor to a plaintiff’s asbestos-related disease. Dixon v. Ford Motor Co., 433 Md. 137 (2013). Science seems not to play well in asbestos cases before the high court of Maryland.


[1] Apparently, the Trust was inappropriately named a Settlement Trust, probably by plaintiffs’ counsel creditors who had apparently hoped it would simply be a cash delivery device.

[2] Colleen K. O’Brien, “Trial Court Erred by Excluding Defense Expert Testimony on Cigarette Smoking As Contributing to Plaintiff’s Lung Cancer” (May 2013); Arlow M. Linton, “Maryland: Failure to Allow Apportionment of Causes of Lung Cancer is Reversible Error” (Oct. 28, 2013).

[3] Colleen K. O’Brien, “Trial Court Properly Excluded Defense Expert Testimony on Cigarette Smoking as Contributing to Plaintiff’s Lung Cancer in Asbestos Case” (Aug. 19, 2014).


				

The Dog That Didn’t Bark – Adverse Inferences for Expert Witnesses

September 13th, 2014

The New Jersey Supreme Court is known for bloated writing, which in the past has gotten the Court in trouble.  Witness the fiasco of the Court’s volubly outrunning its headlights to redefine strict liability to exclude the requirement of a reasonable knowability component in product liability failure-to-warn litigation. Beshada v. Johns-Manville Prods. Corp., 90 N.J. 191, 447 A.2d 539 (1982). Realizing its error, the Court attempted to correct itself a short two years later, but probably only managed to make things worse, in Feldman v. Lederle Labs., 97 N.J. 429, 479 A.2d 374 (1984). Arguably, the prestige of the New Jersey Supreme Court never recovered. See Andrew T. Berry, “Beshada v. Johns-Manville Products Corporation: Revolution-or-Aberration in Products Liability,” 52 Fordham L. Rev. 786 (1984); J. Berman, “Beshada v. Johns-Manville Products Corp.: the function of state of the art evidence in strict products liability,” 10 Am. J. Law & Med. 93 (1984).

Bitten by the Dog That Didn’t Bark

There is a danger is saying too much, and, of course, in not saying the right thing. The New Jersey high Court recently addressed adverse inferences for expert witnesses not called at trial. Washington v. Perez, ___ N.J. ___ (2014). See Bruce D. Greenberg, “Failure to Call an Expert Witness to Testify,” (Sept. 12, 2014). The case was a relative simple vehicular injury case. The defense served two expert witness reports, but did not call either expert witness at trial. In his closing argument, the plaintiff’s lawyer focused on the defense’s uncalled expert witnesses, and went so far as to suggest that defense counsel had lied to the jury. On plaintiff’s request, the court issued an adverse inference charge, instructing the jury that if it reasonably thought defendants should have called Drs. Sharetts and Hayken, then it could infer from the defendants’ not having presented these witnesses, that the missing testimony would have been adverse to defendants’ position at trial. The jury awarded plaintiff substantial damages.

The trial court refused a motion for new trial, but the Appellate Division reversed and remanded for a new trial. Washington v. Perez, 430 N.J. Super. 121, 131 (App. Div. 2013) (holding that trial court had abused its discretion in giving the adverse inference charge). See David R. Kott & Edward J. Fanning Jr., “Adverse Inference for Failing To Call a Witness: What rules apply when a person with material knowledge of a case does not testify?” 212 N.J. Law Journal 783 (June 17, 2013) (reporting on the Appellate Division’s decision). Perhaps not knowing when to stop, plaintiff obtained review in the New Jersey Supreme Court, which then endorsed the Appellate Division’s decision, and held that the giving of the adverse inference charge was error.

As the Appellate Division explained, the kerfuffle started when the plaintiff’s counsel presented a videotaped deposition of plaintiff’s expert witness, Dr. Rosen. In the course of the deposition, Dr. Rosen testified:

“Q. And in both of those reports did Dr. Ha[y]ken indicate what traumatic event or what event he associated the herniated disc that we’ve spoken of and the radiculopathy that we’ve spoken of?

A. Dr. Ha[y]ken states in his report that he feels that the cervical herniated disc and radiculopathy are related to the accident of 12/20/06.”

The defense asked that this Q&A be redacted, and plaintiff’s counsel conceded that Dr. Hayken never so stated in his report. Judge Charles Little, sitting in Burlington County, however, took a “let it all in” approach, despite defense counsel’s statement that he did not plan upon calling Dr. Hayken, and so the elicited testimony would not have been appropriate rebuttal.

The trial judge’s error only compounded. During voir dire of the jury panel, defense counsel had identified his two expert witnesses, and in his opening statement, the defense counsel has told the jury that “the evidence will show that [plaintiff] was not injured in the accident … .” Plaintiff’s counsel ran with the admittedly false testimony of Dr. Rosen, pilloried defense counsel for not calling Dr. Hayken, and argued that Dr. Hayken would have supported the plaintiff’s case.

Despite the Appellate Division’s sure-footed handling of the case, the Supreme Court granted certification, and affirmed in a slip opinion over 40 pages long. Although it took a lot of words, at least in this instance the Court got to the decision right:

“an adverse inference charge should rarely be invoked to address the absence of an expert.”

Slip op. at 3.

Defense counsel had served reports of Drs. Sharetts and Hayken, on plaintiff’s counsel, with a disclaimer that the reports were not defendants’ adoptive admissions. Id. at 6. When objecting to Dr. Rosen’s testimony, defense counsel explained that he did not intend to call his expert witnesses, because plaintiff had failed to prove her case. Id. at 10. Later, however, he claimed that Dr. Hayken was unavailable. Id. at 12.

In any event, Rosen’s dodgy testimony, and the trial court’s equally dodgy awarding of an adverse inference charge, set defense counsel up for a pasting before the jury. After the summations, the trial court let on that it was unhappy with plaintiff’s closing argument that the defense had tried to hide evidence, and that it “should probably grant a new trial,” but the trial court incongruously and circularly denied the new trial because the defense did not present any expert witnesses. Id.

A large part of the bloat in the high court’s opinion is the Court’s exploration of missing witness instructions in civil and criminal cases, for fact and expert witnesses. Id. at 13-28. Given that the Court ultimately held that expert witnesses are different, it might have spared the reader a recitation of the law for fact witnesses. Two thirds into its opinion, the Court finally gets to expert witnesses, but attempts to resolve the conflicting case law and the claims in the case sub judice within the confines of its precedent in State v. Hill, 199 N.J. 545, 974 A.2d 403 (2009). Hill articulated a standard for the propriety of an adverse inference jury instruction in the face of a party’s failure or refusal to call a fact witness.

As the Supreme Court explains, and what we all know, expert witnesses are different. Slip op. at 30. Expert witnesses must be disclosed, and they are subject to heightened discovery in the form of interrogatories and depositions. Second, expert witnesses rarely are in exclusive possession of facts essential to the other side’s case. Id. at 31, 39. Somewhat puzzlingly, the Court offered that parties are not under any obligation to call an expert witnesses, unless their opinions are needed to satisfy an element of the claim or defense. Id. at 32. The same, however, could be said of fact witnesses.

Finally, and most importantly, Court acknowledged that there are “many strategic and practical reasons that may prompt a party who has retained an expert witness to decide not to present the expert’s testimony at trial.” Id. at 33. Expert witnesses are expensive; they are sometimes duplicative; and sometimes they are unavailable. Id. at 35.

According to the Supreme Court, expert witnesses are not generally under a party’s exclusive control, and there is no privilege in a testifying expert witness’s opinion. Id at 36-37. The Court thus suggested that expert witnesses are “available” to the party seeking the adverse inference. Id. at 37.

As with adverse inferences, the most interesting aspects of the Supreme Court’s decision in Washington v. Perez is what the Court did not say. The Court omitted a necessary discussion of how expert witness testimony is presented to a jury or a court, who may, as the finder of fact, accept some, all, or none of the opinion testimony. The party without the burden of proof is free to argue that the adversary’s expert witness was incredible, or that the witness conceded the most important points for the trial, and that calling yet another expert witness in opposition would have wasted the factfinder’s time, and the client’s money.

The availability argument raises the ethical concern of legal counsel attempting ex parte agreements with adversaries’ expert witnesses. And then there is the simple solution that plaintiff’s counsel did not need to elicit imaginary or phony concessions from Dr. Rosen about Dr. Hayken’s report; counsel could have taken Dr. Hayken’s deposition before trial, or during a short recess.

Perhaps even simpler yet, the Court could have (and should have) condemned the admission of Dr. Rosen’s concededly false testimony about what Dr. Hayken’s report stated. The exclusion of this testimony would taken away much of the rationale for plaintiff’s request for the adverse inference instruction.

One way to avoid the request for adverse inference instructions is to announce, say the day before resting, that you have decided not to call an expert witness and that you have released that witness to testify for anyone calling him. This announcement should place the onus on your adversary to ask for time to ask for, or compel, the attendance of the witness. This procedure also preserves the integrity of the process by making clear that your adversary is not free to contact your expert witness until you give permission.

Railroading Scientific Evidence of Causation in Court

August 31st, 2014

Harold Tanfield spent 40 years or so working for Consolidated Rail Corporation (and its predecessors), from 1952 to 1992.  Mr. Tanfield’s widow sued Conrail, under the Federal Employers’ Liability Act (“FELA”), 45 U.S.C.A. §§ 51-60, for negligently overexposing her late husband to diesel fumes, which allegedly caused him to develop lung cancer. Tanfield v. Leigh RR, No. A-4170-12T2, New Jersey Superior Court, App. Div. (Aug. 11, 2014) Slip op. at 3. [cited below as Tanfield].

The trial court granted Conrail summary judgment on grounds that plaintiff failed to show that Conrail had breached a duty of care.  The appellate court reversed and remanded for trial. The Appellate Division’s decision is “per curiam,” and franked “not for publication without the approval of the Appellate Division.” Only two of the usual three appellate judges participated.  The panel decided the case one week after it was submitted.

The plaintiff relied upon two witness, a co-worker of her husband, and an expert witness, Steven R. Tahan, M.D.  Dr. Tahan is a pathologist, an Associate Professor, Department of Pathology, Harvard Medical School, and the Director of Dermatopathology, Beth Israel Deaconess Medical Center.  Dr. Tahan’s website lists melanoma as his principal research interest. A PubMed search reveals no publications on diesel fume, occupational disease, or lung cancer.  Dr. Tahan’s principal research interest, skin pathology, was decidedly not at issue in the Tanfield case.

The panel of the Appellate Division quoted from the relevant paragraphs of Tahan’s report:

“Mr. Tanfield was a railroad worker for 35 years, where he was exposed to a large number of carcinogenic chemicals and fumes, including asbestos, antimony, arsenic, benzene, beryllium, cadmium, carbon disulfide, cyanide, DDT, diesel fumes, diesel fuel, dioxins, ethylbenzene, lead, methylene chloride, mercury, naphthalene, petroleum hydrocarbon, polychlorinated biphenyls, polynuclear aromatic hydrocarbons, toluene, vinyl acetate, and other volatile organics.

I have reviewed the cytology and biopsy slides from the right lung and confirm that he had a poorly differentiated malignant non-small cell carcinoma with both adenocarcinomatous and squamous features.  I have reached the following conclusions to a reasonable degree of medical certainty based on review of the above materials, my education, training, and experience, and review of published studies.

Mr. Tanfield’s more than 35 year substantial occupational exposure to an extensive array of carcinogens and diesel fumes without provision of protective equipment such as masks, respirators, and other filters created a long-term hazard that substantially multiplied his risk for developing lung cancer over the baseline he had as a former smoker.  It is more likely than not that his occupational exposure to diesel fumes and other carcinogenic toxins present in his workplace was a significant causative factor for his development of lung cancer and death from his cancer.”

Tanfield at 6-7.

Mr. Tanfield’s co-worker testified to what appeared to him to be excessive diesel fumes in the workplace, but there is no mention of any quantitative or qualitative evidence to any other lung carcinogen.  The Appellate Division states that the above three paragraphs represent the substance of Dr. Tahan’s report, and so it appears that there is no quantification of Tanfield’s smoking abuse, or the length of time between his discontinuing his smoking and the diagnosis of his lung cancer.  There is no discussion of any support for the alleged interaction between risks, or for any quantification of the extent of his increased risk from his lifestyle choices as opposed to his workplace exposure(s). There is no discussion of what Dr. Tahan visualized in his review of cytology and pathology slides, which permitted him to draw inferences about the actual causes of Mr. Tanfield’s lung cancer.

The trial judge proceeded on the assumption that there was an adequate proffer of expert opinion on causation, but that Dr. Tahan’s opinions on the failure to provide masks or respirators was a “net opinion,” a bit out of Tahan’s area of expertise.  Tanfield at 8. The Appellate Division apparently thought having a skin pathologist opine about the duty of care for a railroad was good enough for government work.  The appellate court gave the widow the benefit of the lower evidentiary threshold for negligence under FELA, which supposedly excuses the lack of an industrial hygiene opinion.  Tanfield at 10.  According to the two-judge panel, “[t]he doctor’s [Tahan’s] opinions are backed by professional literature and by his own considerable years of research and experience.” Tanfield at 11.  The Panel’s statement is all the more remarkable given that Tahan had never published on lung cancer, exposure assessments, or industrial hygiene measures; the vaunted experience of this witness was irrelevant to the issues in the case. Perhaps even more disturbing are the gaps in the proofs concerning the lack of causal connection between many of the alleged exposures and lung cancer generally, any discussion that the level of exposure to diesel fumes, from 1952 to 1992, was such that the railroads knew or should have known that that level of diesel fume caused lung cancer in workers.  And then there is the lurking probability that Mr. Tanfield’s smoking was the sole cause of his lung cancer.

Over 50 years ago, the New York Court of Appeals rejected a claim for leukemia, based upon allegations of benzene exposure, without any quantification of risk from the alleged exposure.  Miller v. National Cabinet Co., 8 N.Y.2d 277, 283-84, 168 N.E.2d 811, 813-15, 204 N.Y.S.2d 129, 132-34, modified on other grounds, 8 N.Y.2d 1025, 70 N.E.2d 214, 206 N.Y.S.2d 795 (1960). It is time to raise the standard for New Jersey courts’ consideration of epidemiologic evidence.

Pritchard v. Dow Agro – Gatekeeping Exemplified

August 25th, 2014

Robert T. Pritchard was diagnosed with Non-Hodgkin’s Lymphoma (NHL) in August 2005; by fall 2005, his cancer was in remission. Mr. Pritchard had been a pesticide applicator, and so, of course, he and his wife sued the deepest pockets around, including Dow Agro Sciences, the manufacturer of Dursban. Pritchard v. Dow Agro Sciences, 705 F.Supp. 2d 471 (W.D.Pa. 2010).

The principal active ingredient of Dursban is chlorpyrifos, along with some solvents, such as xylene, cumene, and ethyltoluene. Id. at 474.  Dursban was licensed for household insecticide use until 2000, when the EPA phased out certain residential applications.  The EPA’s concern, however, was not carcinogenicity:  the EPA categorizes chlorpyrifos as “Group E,” non-carcinogenetic in humans. Id. at 474-75.

According to the American Cancer Society (ACS), the cause or causes of NHL cases are unknown.  Over 60,000 new cases are diagnosed annually, in people from all walks of life, occupations, and lifestyles. The ACS identifies some risk factors, such as age, gender, race, and ethnicity, but the ACS emphasizes that chemical exposures are not proven risk factors or causes of NHL.  See Pritchard, 705 F.Supp. 2d at 474.

The litigation industry does not need scientific conclusions of causal connections; their business is manufacturing certainty in courtrooms. Or at least, the appearance of certainty. The Pritchards found their way to the litigation industry in Pittsburgh, Pennsylvania, in the form of Goldberg, Persky & White, P.C. The Goldberg Persky firm sued Dow Agro, and then put the Pritchards in touch with Dr. Bennet Omalu, to serve as their expert witness.  A lawsuit ensued.

Alas, the Pritchards’ lawsuit ran into a wall, or at least a gate, in the form of Federal Rule of Evidence 702. In the capable hands of Judge Nora Barry Fischer, Rule 702 became an effective barrier against weak and poorly considered expert witness opinion testimony.

Dr. Omalu, no stranger to lost causes, was the medical examiner of San Joaquin County, California, at the time of his engagement in the Pritchard case. After careful consideration of the Pritchards’ claims, Omalu prepared a four page report, with a single citation, to Harrison’s Principles of Internal Medicine.  Id. at 477 & n.6.  This research, however, sufficed for Omalu to conclude that Dursban caused Mr. Pritchard to develop NHL, as well as a host of ailments he had never even sued Dow Agro for, including “neuropathy, fatigue, bipolar disorder, tremors, difficulty concentrating and liver disorder.” Id. at 478. Dr. Omalu did not cite or reference any studies, in his report, to support his opinion that Dursban caused Mr. Pritchard’s ailments.  Id. at 480.

After counsel objected to Omalu’s report, plaintiffs’ counsel supplemented the report with some published articles, including the “Lee” study.  See Won Jin Lee, Aaron Blair, Jane A. Hoppin, Jay H. Lubin, Jennifer A. Rusiecki, Dale P. Sandler, Mustafa Dosemeci, and Michael C. R. Alavanja, “Cancer Incidence Among Pesticide Applicators Exposed to Chlorpyrifos in the Agricultural Health Study,” 96 J. Nat’l Cancer Inst. 1781 (2004) [cited as Lee].  At his deposition, and in opposition to defendants’ 702 motion, Omalu became more forthcoming with actual data and argument.  According to Omalu, the Lee study “the 2004 Lee Study strongly supports a conclusion that high-level exposure to chlorpyrifos is associated with an increased risk of NHL.’’ Id. at 480.

This opinion put forward by Omalu bordered on scientific malpractice.  No; it was malpractice.  The Lee study looked at many different cancer end points, without adjustment for multiple comparisons.  The lack of adjustment means at the very least that any interpretation of p-values or confidence intervals would have to modified to acknowledge the higher rate of random error.  Now for NHL, the overall relative risk (RR) for chlorpyrifos exposure was 1.03, with a 95% confidence interval, 0.62 to 1.70.  Lee at 1783.  In other words, the study that Omalu claimed supported his opinion was about as null a study as can be, with reasonably tight confidence interval that made a doubling of the risk rather unlikely given the sample RR.

If the multiple endpoint testing were not sufficient to dissuade a scientist, intent on supporting the Pritchards’ claims, then the exposure subgroup analysis would have scared any prudent scientist away from supporting the plaintiffs’ claims.  The Lee study authors provided two different exposure-response analyses, one with lifetime exposure and the other with an intensity-weighted exposure, both in quartiles.  Neither analysis revealed an exposure-response trend.  For the lifetime exposure-response trend, the Lee study reported an NHL RR of 1.01, for the highest quartile of chloripyrifos exposure. For the intensity-weighted analysis, for the highest quartile, the authors reported RR = 1.61, with a 95% confidence interval, 0.74 to 3.53).

Although the defense and the district court did not call out Omalu on his fantasy statistical inference, the district judge certainly appreciated that Omalu had no statistically significant associations between chloripyrifos and NHL, to support his opinion. Given the weakness of relying upon a single epidemiologic study (and torturing the data therein), the district court believed that a showing of statistical significance was important to give some credibility to Omalu’s claims.  705 F.Supp. 2d at 486 (citing General Elec. Co. v. Joiner, 522 U.S. 136, 144-46 (1997);  Soldo v. Sandoz Pharm. Corp., 244 F.Supp. 2d 434, 449-50 (W.D. Pa. 2003)).

Figure 3 adapted from Lee

Figure 3 adapted from Lee

What to do when there is really no evidence supporting a claim?  Make up stuff.  Here is how the trial court describes Omalu’s declaration opposing exclusion:

 “Dr. Omalu interprets and recalculates the findings in the 2004 Lee Study, finding that ‘an 80% confidence interval for the highly-exposed applicators in the 2004 Lee Study spans a relative risk range for NHL from slightly above 1.0 to slightly above 2.5.’ Dr. Omalu concludes that ‘this means that there is a 90% probability that the relative risk within the population studied is greater than 1.0’.”

705 F.Supp. 2d at 481 (internal citations omitted); see also id. at 488. The calculations and the rationale for an 80% confidence interval were not provided, but plaintiffs’ counsel assured Judge Fischer at oral argument that the calculation was done using high school math. Id. at 481 n.12. Judge Fischer seemed unimpressed, especially given that there was no record of the calculation.  Id. at 481, 488.

The larger offense, however, was that Omalu’s interpretation of the 80% confidence interval as a probability statement of the true relative risk’s exceeding 1.0, was bogus. Dr. Omalu further displayed his lack of statistical competence when he attempted to defend his posterior probability derived from his 80% confidence interval by referring to a power calculation of a different disease in the Lee study:

“He [Omalu] further declares that ‘‘the authors of the 2004 Lee Study themselves endorse the probative value of a finding of elevated risk with less than a 95% confidence level when they point out that ‘this analysis had a 90% statistical power to detect a 1.5–fold increase in lung cancer incidence’.”

Id. at 488 (court’s quoting of Omalu’s quoting from the Lee study). To quote Wolfgang Pauli, Omalu is so far off that he is “not even wrong.” Lee and colleagues were offering a pre-study power calculation, which they used to justify their looking at the cohort for lung cancer, not NHL, outcomes.  Lee at 1787. The power calculation does not apply to the data observed for lung cancer; and the calculation has absolutely nothing to do with NHL. The power calculation certainly has nothing to do with Omalu’s misguided attempt to offer a calculation of a posterior probability for NHL based upon a subgroup confidence interval.

Given that there were epidemiologic studies available, Judge Fischer noted that expert witnesses were obligated to factor such studies into their opinions. See 705 F.Supp. 2d at 483 (citing Soldo, 244 F.Supp. 2d at 532).  Omalu sins against Rule 702 included his failure to consider any studies other than the Lee study, regardless of how unsupportive the Lee study was of his opinion.  The defense experts pointed to several studies that found lower NHL rates among exposed workers than among controls, and Omalu completely failed to consider and to explain his opinion in the face of the contradictory evidence.  See 705 F.Supp. 2d at 485 (citing Perry v. Novartis Pharm. Corp. 564 F.Supp. 2d 452, 465 (E.D. Pa. 2008)). In other words, Omalu was shown to have been a cherry picker. Id. at 489.

In addition to the abridged epidemiology, Omalu relied upon an analogy between the ethyl-toluene and other solvents that contained benzene rings and benzene itself to argue that these chemicals, supposedly like benzene, cause NHL.  Id. at 487. The analogy was never supported by any citations to published studies, and, of course, the analogy is seriously flawed. Many chemicals, including chemicals made and used by the human body, have benzene rings, without the slightest propensity to cause NHL.  Indeed, the evidence that benzene itself causes NHL is weak and inconsistent.  See, e.g., Knight v. Kirby Inland Marine Inc., 482 F.3d 347 (2007) (affirming the exclusion of Dr. B.S. Levy in a case involving benzene exposure and NHL).

Looking at all the evidence, Judge Fischer found Omalu’s general causation opinions unreliable.  Relying upon a single, statistically non-significant epidemiologic study (Lee), while ignoring contrary studies, was not sound science.  It was not even science; it was courtroom rhetoric.

Omalu’s approach to specific causation, the identification of what caused Mr. Pritchard’s NHL, was equally spurious. Omalu purportedly conducted a “differential diagnosis” or a “differential etiology,” but he never examined Mr. Pritchard; nor did he conduct a thorough evaluation of Mr. Pritchard’s medical records. 705 F.Supp. 2d at 491. Judge Fischer found that Omalu had not conducted a thorough differential diagnosis, and that he had made no attempt to rule out idiopathic or unknown causes of NHL, despite the general absence of known causes of NHL. Id. at 492. The one study identified by Omalu reported a non-statistically significant 60% increase in NHL risk, for a subgroup in one of two different exposure-response analyses.  Although Judge Fischer treated the relative risk less than two as a non-dispositive factor in her decision, she recognized that

“The threshold for concluding that an agent was more likely than not the cause of an individual’s disease is a relative risk greater than 2.0… . When the relative risk reaches 2.0, the agent is responsible for an equal number of cases of disease as all other background causes. Thus, a relative risk of 2.0 … implies a 50% likelihood that an exposed individual’s disease was caused by the agent. A relative risk greater than 2.0 would permit an inference that an individual plaintiff’s disease was more likely than not caused by the implicated agent.”

Id. at 485-86 (quoting from Reference Manual on Scientific Evidence at 384 (2d ed. 2000)).

Left with nowhere to run, plaintiffs’ counsel swung for the bleachers by arguing that the federal court, sitting in diversity, was required to apply Pennsylvania law of evidence because the standards of Rule 702 constitute “substantive,” not procedural law. The argument, which had been previously rejected within the Third Circuit, was as legally persuasive as Omalu’s scientific opinions.  Judge Fischer excluded Omalu’s proffered opinions and granted summary judgment to the defendants. The Third Circuit affirmed in a per curiam decision. 430 Fed. Appx. 102, 2011 WL 2160456 (3d Cir. 2011).

Practical Evaluation of Scientific Claims

The evaluative process that took place in the Pritchard case missed some important details and some howlers committed by Dr. Omalu, but it was more than good enough for government work. The gatekeeping decision in Pritchard was nonetheless the target of criticism in a recent book.

Kristin Shrader-Frechette (S-F) is a professor of science who wants to teach us how to expose bad science. S-F has published, or will soon publish, a book that suggests that philosophy of science can help us expose “bad science.”  See Kristin Shrader-Frechette, Tainted: How Philosophy of Science Can Expose Bad Science (Oxford U.P. 2014)[cited below at Tainted; selections available on Google books]. S-F’s claim is intriguing, as is her move away from the demarcation problem to the difficult business of evaluation and synthesis of scientific claims.

In her introduction, S-F tells us that her book shows “how practical philosophy of science” can counteract biased studies done to promote special interests and PROFITS.  Tainted at 8. Refreshingly, S-F identifies special-interest science, done for profit, as including “individuals, industries, environmentalists, labor unions, or universities.” Id. The remainder of the book, however, appears to be a jeremiad against industry, with a blind eye towards the litigation industry (plaintiffs’ bar) and environmental zealots.

The book promises to address “public concerns” in practical, jargon-free prose. Id. at 9-10. Some of the aims of the book are to provide support for “rejecting demands for only human evidence to support hypotheses about human biology (chapter 3), avoiding using statistical-significance tests with observational data (chapter 12), and challenging use of pure-science default rules for scientific uncertainty when one is doing welfare-affecting science (chapter 14).”

Id. at 10. Hmmm.  Avoiding statistical significance tests for observational data?!?  If avoided, what does S-F hope to use to assess random error?

And then S-F refers to plaintiffs’ hired expert witness (from the Milward case), Carl Cranor, as providing “groundbreaking evaluations of causal inferences [that] have helped to improve courtroom verdicts about legal liability that otherwise put victims at risk.” Id. at 7. Whether someone is a “victim” and has been “at risk” turns on assessing causality. Cranor is not a scientist, and his philosophy of science turns of “weight of the evidence” (WOE), a subjective, speculative approach that is deaf, dumb, and blind to scientific validity.

There are other “teasers,” in the introduction to Tainted.  S-F advertises that her Chapter 5 will teach us that “[c]ontrary to popular belief, animal and not human data often provide superior evidence for human-biological hypotheses.”  Tainted at 11. Chapter 6 will show that“[c]ontrary to many physicists’ claims, there is no threshold for harm from exposure to ionizing radiation.” Id.  S-F tells us that her Chapter 7 will criticize “a common but questionable way of discovering hypotheses in epidemiology and medicine—looking at the magnitude of some effect in order to discover causes. The chapter shows instead that the likelihood, not the magnitude, of an effect is the better key to causal discovery.” Id. at 13. Discovering hypotheses — what is that about? You might have thought that hypotheses were framed from observations and then tested.

Which brings us to the trailer for Chapter 8, in which S-F promises to show that “[c]ontrary to standard statistical and medical practice, statistical-significance tests are not causally necessary to show medical and legal evidence of some effect.” Tainted at 11. Again, the teaser raises lots of questions such as what could S-F possibly mean when she says statistical tests are not causally necessary to show an effect.  Later in the introduction, S-F says that her chapter on statistics “evaluates the well-known statistical-significance rule for discovering hypotheses and shows that because scientists routinely misuse this rule, they can miss discovering important causal hypotheses. Id. at 13. Discovering causal hypotheses is not what courts and regulators must worry about; their task is to establish such hypotheses with sufficient, valid evidence.

Paging through the book reveals that a rhetoric that is thick and unremitting, with little philosophy of science or meaningful advice on how to evaluate scientific studies.  The statistics chapter calls out, and lo, it features a discussion of the Pritchard case. See Tainted, Chapter 8, “Why Statistics Is Slippery: Easy Algorithms Fail in Biology.”

The chapter opens with an account of German scientist Fritz Haber’s development of organophosphate pesticides, and the Nazis use of related compounds as chemical weapons.  Tainted at 99. Then, in a fevered non-sequitur and rhetorical flourish, S-F states, with righteous indignation, that although the Nazi researchers “clearly understood the causal-neurotoxic effects of organophosphate pesticides and nerve gas,” chemical companies today “claim that the causal-carcinogenic effects of these pesticides are controversial.” Is S-F saying that a chemical that is neurotoxic must be carcinogenic for every kind of human cancer?  So it seems.

Consider the Pritchard case.  Really, the Pritchard case?  Yup; S-F holds up the Pritchard case as her exemplar of what is wrong with civil adjudication of scientific claims.  Despite the promise of jargon-free language, S-F launches into a discussion of how the judges in Pritchard assumed that statistical significance was necessary “to hypothesize causal harm.”  Tainted at 100. In this vein, S-F tells us that she will show that:

“the statistical-significance rule is not a legitimate requirement for discovering causal hypotheses.”

Id. Again, the reader is left to puzzle why statistical significance is discussed in the context of hypothesis discovery, whatever that may be, as opposed to hypothesis testing or confirmation. And whatever it may be, we are warned that “unless the [statistical significance] rule is rejected as necessary for hypothesis-discovery, it will likely lead to false causal claims, questionable scientific theories, and massive harm to innocent victims like Robert Pritchard.”

Id. S-F is decidedly not adverting to Mr. Pritichard’s victimization by the litigation industry and the likes of Dr. Omalu, although she should. S-F not only believes that the judges in Pritchard bungled their gatekeeping wrong, she knows that Dr. Omalu was correct, and the defense experts wrong, and that Pritchard was a victim of Dursban and of questionable scientific theories that were used to embarrass Omalu and his opinions.

S-F promised to teach her readers how to evaluate scientific claims and detect “tainted” science, but all she delivers here is an ipse dixit.  There is no discussion of the actual measurements, extent of random error, or threats to validity, for studies cited either by the plaintiffs or the defendants in Pritchard.  To be sure, S-F cites the Lee study in her endnotes, but she never provides any meaningful discussion of that study or any other that has any bearing on chlorpyrifos and NHL.  S-F also cited two review articles, the first of which provides no support for her ipse dixit:

“Although mutagenicity and chronic animal bioassays for carcinogenicity of chlorpyrifos were largely negative, a recent epidemiological study of pesticide applicators reported a significant exposure response trend between chlorpyrifos use and lung and rectal cancer. However, the positive association was based on small numbers of cases, i.e., for rectal cancer an excess of less than 10 cases in the 2 highest exposure groups. The lack of precision due to the small number of observations and uncertainty about actual levels of exposure warrants caution in concluding that the observed statistical association is consistent with a causal association. This association would need to be observed in more than one study before concluding that the association between lung or rectal cancer and chlorpyrifos was consistent with a causal relationship.

There is no evidence that chlorpyrifos is hepatotoxic, nephrotoxic, or immunotoxic at doses less than those that cause frank cholinesterase poisoning.”

David L. Eaton, Robert B. Daroff, Herman Autrup, James Bridges, Patricia Buffler, Lucio G. Costa, Joseph Coyle, Guy McKhann, William C. Mobley, Lynn Nadel, Diether Neubert, Rolf Schulte-Hermann, and Peter S. Spencer, “Review of the Toxicology of Chlorpyrifos With an Emphasis on Human Exposure and Neurodevelopment,” 38 Critical Reviews in Toxicology 1, 5-6(2008).

The second cited review article was written by clinical ecology zealot[1], William J. Rea. William J. Rea, “Pesticides,” 6 Journal of Nutritional and Environmental Medicine 55 (1996). Rea’s article does not appear in Pubmed.

Shrader-Frechette’s Criticisms of Statistical Significance Testing

What is the statistical significance against which S-F rails? She offers several definitions, none of which is correct or consistent with the others.

“The statistical-significance level p is defined as the probability of the observed data, given that the null hypothesis is true.8

Tainted at 101 (citing D. H. Johnson, “What Hypothesis Tests Are Not,” 16 Behavioral Ecology 325 (2004). Well not quite; attained significance probability is the probability of data observed or those more extreme, given the null hypothesis.  A Tainted definition.

Later in Chapter 8, S-F discusses significance probability in a way that overtly commits the transposition fallacy, not a good thing to do in a book that sets out to teach how to evaluate scientific evidence:

“However, typically scientists view statistical significance as a measure of how confidently one might reject the null hypothesis. Traditionally they have used a 0.05 statistical-significance level, p < or = 0.05, and have viewed the probability of a false-positive (incorrectly rejecting a true null hypothesis), or type-1, error as 5 percent. Thus they assume that some finding is statistically significant and provides grounds for rejecting the null if it has at least a 95-percent probability of not being due to chance.

Tainted at 101. Not only does the last sentence ignore the extent of error due to bias or confounding, it erroneously assigns a posterior probability that is the complement of the significance probability.  This error is not an isolated occurrence; here is another example:

“Thus, when scientists used the rule to examine the effectiveness of St. John’s Wort in relieving depression,14 or when they employed it to examine the efficacy of flutamide to treat prostate cancer,15 they concluded the treatments were ineffective because they were not statistically significant at the 0.05 level. Only at p < or = 0.14 were the results statistically significant. They had an 86-percent chance of not being due to chance.16

Tainted at 101-02 (citing papers by Shelton (endnote 14)[2], by Eisenberger (endnote 15) [3], and Rothman’s text (endnote 16)[4]). Although Ken Rothman has criticized the use of statistical significance tests, his book surely does not interpret a p-value of 0.14 as an 86% chance that the results were not due to chance.

Although S-F previous stated that statistical significance is interpreted as the probability that the null is true, she actually goes on to correct the mistake, sort of:

“Requiring the statistical-significance rule for hypothesis-development also is arbitrary in presupposing a nonsensical distinction between a significant finding if p = 0.049, but a nonsignificant finding if p = 0. 051.26 Besides, even when one uses a 90-percent (p < or = 0.10), an 85-percent (p < or = 0.15), or some other confidence level, it still may not include the null point. If not, these other p values also show the data are consistent with an effect. Statistical-significance proponents thus forget that both confidence levels and p values are measures of consistency between the data and the null hypothesis, not measures of the probability that the null is true. When results do not satisfy the rule, this means merely that the null cannot be rejected, not that the null is true.”

Tainted at 103.

S-F’s repeats some criticisms of significance testing, most of which involve their own misunderstandings of the concept.  It hardly suffices to argue that evaluating the magnitude of random error is worthless because it does not measure the extent of bias and confounding.  The flaw lies in those who would interpret the p-value as the sole measure of error involved in a measurement.

S-F takes the criticisms of significance probability to be sufficient to justify an alternative approach: evaluating causal hypotheses “on a preponderance of evidence,47 whether effects are more likely than not.”[5] Here citations, however, do not support the notion that an overall assessment of the causal hypothesis is a true alternative of statistical testing, but rather only a later step in the causal assessment, which presupposes the previous elimination of random variability in the observed associations.

S-F compounds her confusion by claiming that this purported alternative is superior to significance testing or any evaluation of random variability, and by noting that juries in civil cases must decide causal claims on the preponderance of the evidence, not on attained significance probabilities:

“In welfare-affecting areas of science, a preponderance-of-evidence rule often is better than a statistical-significance rule because it could take account of evidence based on underlying mechanisms and theoretical support, even if evidence did not satisfy statistical significance. After all, even in US civil law, juries need not be 95 percent certain of a verdict, but only sure that a verdict is more likely than not. Another reason for requiring the preponderance-of-evidence rule, for welfare-related hypothesis development, is that statistical data often are difficult or expensive to obtain, for example, because of large sample-size requirements. Such difficulties limit statistical-significance applicability. ”

Tainted at 105-06. S-F’s assertion that juries need not have 95% certainty in their verdict is either a misunderstanding or a misrepresentation of the meaning of a confidence interval, and a conflation of two very kinds of probability or certainty.  S-F invites a reading that commits the transposition fallacy by confusing the probability involved in a confidence interval with that involved in a posterior probability.  S-F’s claim that sample size requirements often limit the ability to use statistical significance evaluations is obviously highly contingent upon the facts of case, but in civil cases, such as Pritchard, this limitation is rarely at play.  Of course, if the sample size is too small to evaluate the role of chance, then a scientist should probably declare the evidence too fragile to support a causal conclusion.

S-F also postulates that that a posterior probability rather than a significance probability approach would “better counteract conflicts of interest that sometimes cause scientists to pay inadequate attention to public-welfare consequences of their work.” Tainted at 106. This claim is a remarkable assertion, which is not supported by any empirical evidence.  The varieties of evidence that go into an overall assessment of a causal hypothesis are often quantitatively incommensurate.  The so-called preponderance-of-the-evidence described by S-F is often little more than a subjective overall assessment of weight of the evidence.  The approving citations to the work of Carl Cranor support interpreting S-F to endorse this subjective, anything-goes approach to weight of the evidence.  As for WOE eliminating inadequate attention to “public welfare,” S-F’s citations actually suggest the opposite. S-F’s citations to the 1961 reviews by Wynder and by Little illustrate how subjective narrative reviews can be, with diametrically opposed results.  Rather than curbing conflicts of interest, these subjective, narrative reviews illustrate how contrary results may be obtained by the failure to pre-specify criteria of validity, and inclusion and exclusion of admissible evidence. Still, S-F asserts that “up to 80 percent of welfare-related statistical studies have false-negative or type-II errors, failing to reject a false null.” Tainted at 106. The support for this assertion is a citation to a review article by David Resnik. See David Resnik, “Statistics, Ethics, and Research: An Agenda for Education and Reform,” 8 Accountability in Research 163, 183 (2000). Resnik’s paper is a review article, not an empirical study, but at the page cited by S-F, Resnik in turn cites to well-known papers that present actual data:

“There is also evidence that many of the errors and biases in research are related to the misuses of statistics. For example, Williams et al. (1997) found that 80% of articles surveyed that used t-tests contained at least one test with a type II error. Freiman et al. (1978)  * * *  However, empirical research on statistical errors in science is scarce, and more work needs to be done in this area.”

Id. The papers cited by Resnik, Williams (1997)[6] and Freiman (1978)[7] did identify previously published studies that over-interpreted statistically non-significant results, but the identified type-II errors were potential errors, not ascertained errors, because the authors made no claim that every non-statistically significant result actually represented a missed true association. In other words, S-F is not entitled to say that these empirical reviews actually identified failures to reject fall null hypotheses. Furthermore, the empirical analyses in the studies cited by Resnik, who was in turn cited by S-F, did not look at correlations between alleged conflicts of interest and statistical errors. The cited research calls for greater attention to proper interpretation of statistical tests, not for their abandonment.

In the end, at least in the chapter on statistics, S-F fails to deliver much if anything on her promise to show how to evaluate science from a philosophic perspective.  Her discussion of the Pritchard case is not an analysis; it is a harangue. There are certainly more readable, accessible, scholarly, and accurate treatments of the scientific and statistical issues in this book.  See, e.g., Michael B. Bracken, Risk, Chance, and Causation: Investigating the Origins and Treatment of Disease (2013).


[1] Not to be confused with the deceased federal judge by the same name, William J. Rea. William J. Rea, 1 Chemical Sensitivity – Principles and Mechanisms (1992); 2 Chemical Sensitivity – Sources of Total Body Load (1994),  3 Chemical Sensitivity – Clinical Manifestation of Pollutant Overload (1996), 4 Chemical Sensitivity – Tools of Diagnosis and Methods of Treatment (1998).

[2] R. C. Shelton, M. B. Keller, et al., “Effectiveness of St. John’s Wort in Major Depression,” 285 Journal of the American Medical Association 1978 (2001).

[3] M. A. Eisenberger, B. A. Blumenstein, et al., “Bilateral Orchiectomy With or Without Flutamide for Metastic [sic] Prostate Cancer,” 339 New England Journal of Medicine 1036 (1998).

[4] Kenneth J. Rothman, Epidemiology 123–127 (NY 2002).

[5] Endnote 47 references the following papers: E. Hammond, “Cause and Effect,” in E. Wynder, ed., The Biologic Effects of Tobacco 193–194 (Boston 1955); E. L. Wynder, “An Appraisal of the Smoking-Lung-Cancer Issue,”264  New England Journal of Medicine 1235 (1961); see C. Little, “Some Phases of the Problem of Smoking and Lung Cancer,” 264 New England Journal of Medicine 1241 (1961); J. R. Stutzman, C. A. Luongo, and S. A McLuckey, “Covalent and Non-Covalent Binding in the Ion/Ion Charge Inversion of Peptide Cations with Benzene-Disulfonic Acid Anions,” 47 Journal of Mass Spectrometry 669 (2012). Although the paper on ionic charges of peptide cations is unfamiliar, the other papers do not eschew traditional statistical significance testing techniques. By the time these early (1961) reviews were written, the association that was reported between smoking and lung cancer was clearly accepted as not likely explained by chance.  Discussion focused upon bias and potential confounding in the available studies, and the lack of animal evidence for the causal claim.

[6] J. L. Williams, C. A. Hathaway, K. L. Kloster, and B. H. Layne, “Low power, type II errors, and other statistical problems in recent cardiovascular research,” 42 Am. J. Physiology Heart & Circulation Physiology H487 (1997).

[7] Jennie A. Freiman, Thomas C. Chalmers, Harry Smith and Roy R. Kuebler, “The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial: survey of 71 ‛negative’ trials,” 299 New Engl. J. Med. 690 (1978).

Twerski’s Defense of Daubert

July 6th, 2014

Professor Aaron D. Twerski teaches torts and products liability at the Brooklyn Law School.  Along with a graduating student, Lior Sapir, Twerski has published an article in which the authors mistakenly asseverate that “[t]his is not another article about Daubert.” Aaron D. Twerski & Lior Sapir, “Sufficiency of the Evidence Does Not Meet Daubert Standards: A Critique of the Green-Sanders Proposal,” 23 Widener L.J. 641, 641 (2014) [Twerski & Sapir].

A few other comments.

1. The title of the article.  True, true, and immaterial. As Professor David Bernstein has pointed out many times, Daubert is no longer the law; Federal Rule of Evidence 702, a statute, is the law.  Just as the original Rule 702 superseded Frye in 1975, a revised Rule 702, in 2000, superseded Daubert in 1975. See David E. Bernstein, “The Misbegotten Judicial Resistance to the Daubert Revolution,” 89 Notre Dame L. Rev. 27 (2013).

2. Twerski and Sapir have taken aim at a draft paper by Professors Green and Sanders, who also presented similar ideas at a workshop in March 2012, in Spain. The Green-Sanders manuscript is available on line. Michael D. Green & Joseph Sanders, “Admissibility Versus Sufficiency: Controlling the Quality of Expert Witness Testimony in the United States,” (March 5, 2012) <downloaded on March 25, 2012>. This article appears to have matured since spring 2012, but it has never progressed to parturition.  Professor Green’s website suggests a mutated version is in the works:  “The Daubert Sleight of Hand: Substituting Reliability, Methodology, and Reasoning for an Old Fashioned Sufficiency of the Evidence Test.”

Indeed, the draft paper is a worthwhile target. SeeAdmissibility versus Sufficiency of Expert Witness Evidence” (April 18, 2012).  Green and Sanders pursue a reductionist approach to Rule 702, which is unfaithful to the letter and spirit of the law.

3. In their critique of Green and Sanders, Twerski and Sapir get some issues wrong. First they insist upon talking about Daubert criteria.  The “criteria” were never really criteria, and as Bernstein’s scholarship establishes, it is time to move past Daubert.

4. Twerski and Sapir assert that Daubert imposes a substantial or heavy burden of proof upon the proponent of expert witness opinion testimony:

“The Daubert trilogy was intended to set a formidable standard for admissibility before one entered the thicket of evaluating whether it was sufficient to serve as grounds for recovery.”

Twerski & Sapir at 648.

Daubert instituted a “high threshold of reliability”.

Twerski & Sapir at 649.

“But, the message from the Daubert trilogy is unmistakable: a court must have a high degree of confidence in the integrity of scientific evidence before it qualifies for consideration in any formal test to be utilized in litigation.”

Twerski & Sapir at 650.

“The Daubert standard is anything but minimal.”

Twerski & Sapir at 651.

Twerski and Sapir never explain whence comes “high,” “formidable,” and “anything but minimal.” To be sure, the Supreme Court noted that “[s]ince Daubert . . . parties relying on expert evidence have had notice of the exacting standards of reliability such evidence must meet.” Weisgram v. Marley Co., 528 U.S. 440, 455 (2000) (emphasis added). An exacting standard, however, is not necessarily a heavy burden.  It may be that the exacting standard is infrequently satisfied because the necessary evidence and inferences, of sufficiency quality and validity, are often missing. The truth is that science is often in the no-man’s land of indeterminate, inconclusive, and incomplete. Nevertheless, Twerski and Sapir play into the hands of the reductionist Green-Sanders’ thesis by talking about what appears to be a [heavy] burden of proof and the “weight of evidence” needed to sustain the burden.

5. Twerski and Sapir obviously recognize that reliability is different from sufficiency, but they miss the multi-dimensional aspect of expert witness opinion testimony.  Consider their assertion that:

“[t]he Court of Appeals for the Eleventh Circuit in Joiner had not lost its senses when it relied on animal studies to prove that PCBs cause lung cancer. If the question was whether any evidence viewed in the light most favorable to plaintiff supported liability, the answer was probably yes.”

Twerski & Sapir at 649; see Joiner v. Gen. Electric Co., 78 F.3d 524, 532 (11th Cir. 1996) rev’d, 522 U.S. 136 (1997).

The imprecision in thinking about expert witness testimony obscures what happened in Joiner, and what must happen under the structure of the evidence statutes (or case law).  The Court of Appeals never relied upon animal studies; nor did the district court below.  Expert witnesses relied upon animal studies, and other studies, and then offered an opinion that these studies “prove” PCBs cause human lung cancer, and Mr. Joiner’s lung cancer in particular.  Those opinions, which the Eleventh Circuit would have taken at face value, would be sufficient to support submitting the case to jury.  Indeed, courts that evade the gatekeeping requirements of Rule 702 routinely tout the credentials of the expert witnesses, recite that they have used science in some sense, and that criticisms of their opinions “go to the weight not the admissibility” of the opinions.  These are, of course, evasions used to dodge Daubert and Rule 702. They are evasions because the science recited is at a very high level of abstraction (“I relied upon epidemiology”), because credentials are irrelevant, and because “weight not the admissibility” is a conclusion not a reason.

Some of the issues obscured by the reductionist weight-of-the-evidence approach are the internal and external validity of the studies cited, whether the inferences drawn from the studies cited are valid and accurate, and whether the method of synthesizing  conclusion from disparate studies is appropriate. These various aspects of an evidentiary display cannot be reduced to a unidimensional “weight.” Consider how many observational studies suggested, some would say demonstrated, that beta carotene supplements reduced the risk of lung cancer, only to be pushed aside by one or two randomized clinical trials.

6. Twerski and Sapir illustrate the crucial point that gatekeeping judges must press beyond the conclusory opinions by exploring the legal controversy over Parlodel and post-partum strokes.  Twerski & Sapir at 652. Their exploration takes them into some of the same issues that confronted the Supreme Court in Joiner:  extrapolations or “leaps of faith” between different indications, different species, different study outcomes, between surrogate end points and the end point of interest, between very high to relatively low therapeutic doses. Twerski and Sapir correctly discern that these various issues cannot be simply subsumed under weight or sufficiency.

7. Professors Green and Sanders have published a brief reply, in which they continue their “weight of the evidence” reductionist argument. Michael D. Green & Joseph Sanders, “In Defense of Sufficiency: A Reply to Professor Twerski and Mr. Sapir,” 23 Widener L.J. 663 (2014). Green and Sanders restate their position that courts can, should, and do sweep all the nuances of evidence and inference validity into a single metric – weight and sufficiency – to adjudicate so-called Daubert challenges.  What Twerski and Sapir seem to have stumbled upon is that Green and Sanders are not engaged in a descriptive enterprise; they are prescribing a standard that abridges and distorts the law and best practice in order to ensure that dubious causal claims are submitted to the finder of fact.

Zoloft MDL Excludes Proffered Testimony of Anick Bérard, Ph.D.

June 27th, 2014

Anick Bérard is a Canadian perinatal epidemiologist in the Université de Montréal.  Bérard was named by plaintiffs’ counsel in the Zoloft MDL to offer an opinion that selective serotonin reuptake inhibitor (SSRI) antidepressants as a class, and Zoloft (sertraline) specifically, cause a wide range of birth defects. Bérard previously testified against GSK about her claim that paroxetine, another SSRI antidepressant is a teratogen.

Pfizer challenged Bérard’s proffered testimony under Federal Rules of Evidence 104(a), 702, 703, and 403.  Today, the Zoloft MDL transferee court handed down its decision to exclude Dr. Bérard’s testimony at the time of trial.  In re Zoloft (Sertraline Hydrochloride) Prods. Liab. Litig., MDL 2342, Document 979 (June 27, 2014).  The MDL court acknowledged the need to consider the selectivity (“cherry picking”) of studies upon which Dr. Bérard relied, as well as her failure to consider multiple comparisons, ascertainment bias, confounding by indication, and lack of replication of specific findings across the different SSRI medications, and across studies. Interestingly, the MDL court recognized that Dr. Bérard’s critique of studies as “underpowered” was undone by her failure to consider available meta-analyses or to conduct one of her own. The MDL court seemed especially impressed by Dr. Bérard’s having published several papers that rejected a class effect of teratogenicity for all SSRIs, as recently as 2012, while failing to identify anything that was published subsequently that could explain her dramatic change in opinion for litigation.

Substituting Risk for Specific Causation

June 15th, 2014

Specious, Speculative, Spurious, and Sophistical

Some legal writers assert that all evidence is ultimately “probable,” but that assertion appears to be true only to the extent that the evidentiary support for any claim can be mapped on scale from 0 to 1, much as probability is.  Probability thus finds its way into discussions of burdens of persuasion as requiring the claim to be shown more probably than not, and expert witness certitude as requiring “reasonable degree of scientific probability.”

There is a contrary emphasis in the law on “actual truth,” which is different from “mere probability.”  The rejection of probabilism can be seen in some civil cases, in which courts have emphasized the need for individualistic data and conclusions, beyond generalizations that might be made about groups that clearly encompass the individual at issue. For example, the Supreme Court has held that charging more for funding a woman’s pension than a man’s is discriminatory because not all women will outlive all men, or the men’s average life expectancy. City of Los Angeles Dep’t of Water and Power v. Manhart, 435 U.S. 702, 708 (1978) (“Even a true generalization about a class is an  insufficient reason for disqualifying an individual to whom the generalization does not apply.”). See also El v. Southeastern Pennsylvania Transportation Authority, 479 F.3d 232, 237 n.6 (3d Cir. 2007) (“The burden of persuasion … is the obligation to convince the factfinder at trial that a litigant’s necessary propositions of fact are indeed true.”).

Specific causation is the soft underbelly of the toxic tort world, in large measure because courts know that risk is not specific causation. In the context of risk of disease, which is usually based upon a probabilistic group assessment, courts occasionally distinguish between risk and specific causation. SeeProbabilism Case Law” (Jan. 28, 2013) (collecting cases for and against probabilism).

In In re Fibreboard Corp., 893 F. 2d 706, 711-12 (5th Cir. 1990), the court rejected a class action approach to litigating asbestos personal injury claims because risk could not substitute for findings of individual causation:

“That procedure cannot focus upon such issues as individual causation, but ultimately must accept general causation as sufficient, contrary to Texas law. It is evident that these statistical estimates deal only with general causation, for ‘population-based probability estimates do not speak to a probability of causation in any one case; the estimate of relative risk is a property of the studied population, not of an individual’s case.’ This type of procedure does not allow proof that a particular defendant’s asbestos ‘really’ caused a particular plaintiff’s disease; the only ‘fact’ that can be proved is that in most cases the defendant’s asbestos would have been the cause.”

Id. at 711-12 (citing Steven Gold, “Causation in Toxic Torts: Burdens of Proof, Standards of Persuasion, and Statistical Evidence,” 96 Yale L.J. 376, 384, 390 (1986). See also Guinn v. AstraZeneca Pharms., 602 F.3d 1245, 1255 (11th Cir. 2010) (“An expert, however, cannot merely conclude that all risk factors for a disease are substantial contributing factors in its development. ‘The fact that exposure to [a substance] may be a risk factor for [a disease] does not make it an actual cause simply because [the disease] developed.’”) (internal citation omitted).

Specific causation is the soft underbelly of the toxic tort world, in large measure because courts know that risk is not specific causation. The analytical care of the Guinn case and others is often abandoned when it will stand in the way of compensation. The conflation of risk and (specific) causation is prevalent precisely because in many cases there is no scientific or medical way to discern what antecedent risks actually played a role in causing an individual’s disease.  Opinions about specific causation are thus frequently devoid of factual or logical support, and are propped up solely by hand waving about differential etiology and inference to the best explanation.

In the scientific world, most authors recognize that risk, even if real and above baseline, regardless of magnitude, does not support causal attribution in a specific case.[1]  Sir Richard Doll, who did so much to advance the world’s understanding of asbestosis as a cause of lung cancer, issued a caveat about the limits of specific causation inference. Richard Doll, “Proof of Causality: Deduction from Epidemiological Observation,” 45 Perspectives in Biology & Medicine 499, 500 (2002) (“That asbestos is a cause of lung cancer in this practical sense is incontrovertible, but we can never say that asbestos was responsible for the production of the disease in a particular patient, as there are many other etiologically significant agents to which the individual may have been exposed, and we can speak only of the extent to which the risk of the disease was increased by the extent of his or her exposure.”)

Similarly, Kenneth Rothman, a leading voice among epidemiologists, cautioned against conflating epidemiologic inferences about groups with inferences about causes in individuals. Kenneth Rothman, Epidemiology: An Introduction 44 (Oxford 2002) (“An elementary but essential principal that epidemiologists must keep in mind is that a person may be exposed to an agent and then develop disease without there being any causal connection between exposure and disease.”  … “In a courtroom, experts are asked to opine whether the disease of a given patient has been caused by a specific exposure.  This approach of assigning causation in a single person is radically different from the epidemiologic approach, which does not attempt to attribute causation in any individual instance.  Rather, the epidemiologic approach is to evaluate the proposition that the exposure is a cause of the disease in a theoretical sense, rather than in a specific person.”) (emphasis added).

The late David Freedman, who was the co-author of the chapters on statistics in all three editions of the Reference Manual on Scientific Evidence, was also a naysayer when it came to transmuting risk into cause:

“The scientific connection between specific causation and a relative risk of two is doubtful. *** Epidemiologic data cannot determine the probability of causation in any meaningful way because of individual differences.”

David Freedman & Philip Stark, “The Swine Flu Vaccine and Guillaine-Barré Syndrome:  A Case Study in Relative Risk and Specific Causation,” 64 Law & Contemporary Problems 49, 61 (2001) (arguing that proof of causation in a specific case, even starting with a relative risk of four, was “unconvincing”; citing Manko v. United States, 636 F. Supp. 1419, 1437 (W.D. Mo. 1986) (noting relative risk of 3.89–3.92 for GBS from swine-flu vaccine), aff’d in part, 830 F.2d 831 (8th Cir. 1987)).

Graham Colditz, who testified for plaintiffs in the hormone therapy litigation, similarly has taught that an increased risk of disease cannot be translated into the “but-for” standard of causation.  Graham A. Colditz, “From epidemiology to cancer prevention: implications for the 21st Century,” 18 Cancer Causes Control 117, 118 (2007) (“Knowledge that a factor is associated with increased risk of disease does not translate into the premise that a case of disease will be prevented if a specific individual eliminates exposure to that risk factor. Disease pathogenesis at the individual level is extremely complex.”)

Another epidemiologist, who wrote the chapter in the Federal Judicial Center’s Reference Manual on Scientific Evidence, on epidemiology, put the matter thus:

“However, the use of data from epidemiologic studies is not without its problems. Epidemiology answers questions about groups, whereas the court often requires information about individuals.

Leon Gordis, Epidemiology 362 (5th ed. 2014) (emphasis in original).

=========================================================

In New Jersey, an expert witness’s opinion that lacks a factual foundation is termed a “net opinion.” Polzo v. County of Essex, 196 N.J. 569, 583 (2008) (explaining New Jersey law’s prohibition against “net opinions” and “speculative testimony”). Under federal law, Rule 702, such an opinion is simply called inadmissible.

Here is an interesting example of a “net opinion” from an expert witness, in the field of epidemiology, who has testified in many judicial proceedings:

 

                                                                                          November 12, 2008

George T. Brugess, Esq.
Hoey & Farina, Attorneys at Law
542 South Dearborn Street, Suite 200
Chicago, IL 60605

Ref: Oscar Brooks v. Ingram Barge and Jantran Inc.

* * * *

Because [the claimant] was employed 28 years, he falls into the greater than 20 years railroad employment category (see Table 3 of Garshick’s 2004 paper) which shows a significant risk for lung cancer that ranges from 1.24 to 1.50. This means that his diesel exposure was a significant factor in his contracting lung cancer. His extensive smoking was also a factor in his lung cancer, and diesel exposure combined with smoking is an explanation for the relatively early age, 61 years old, of his diagnosis.

Now assuming that diesel exposure truly causes lung cancer, what was the basis for this witness (David F. Goldsmith, PhD) to opine that diesel exposure was a “significant factor” in the claimant’s developing lung cancer?  None really.  There was no basis in the report, or in the scientific data, to transmute an exposure that yielded a risk ratio of 1.24 to 1.50 for lung cancer, in a similarly exposed population to diesel emissions, into a “significant factor.” The claimant’s cancer may have arisen from background, baseline risk.  The cancer may have arisen from the risk due to smoking, which would have been on the order of a 2,000% increase, or so.  The cancer may have arisen from the claimed carcinogenicity of diesel emissions, on the order of 25 to 50%, which was rather insubstantial compared with his smoking risk.  Potentially, the cancer arose from a combination of the risk from both diesel emissions and tobacco smoking. In the population of men who looked like Mr. Oscar Brooks, by far, the biggest reduction in incidence would be achieved by removing tobacco smoking.

There were no biomarkers that identified the claimant’s lung cancer as having been caused by diesel emissions.  The expert witness’s opinion was nothing more than an ipse dixit that equated a risk, and a rather small risk, with specific causation.  Notice how a 24% increased risk from diesel emissions was a “significant factor,” but the claimant’s smoking history was merely “a factor.”

Goldsmith’s report on specific causation was a net opinion that exemplifies what is wrong with a legal system that encourages and condones baseless expert witness testimony. In Agent Orange, Judge Weinstein pointed out that the traditional judicial antipathy to probabilism would mean no recovery in many chemical and medicinal exposure cases.  If the courts lowered their scruples to permit recovery on a naked statistical inference of greater than 50%, from relative risks greater than two, some cases might remain viable (but alas not the Agent Orange case itself). Judge Weinstein was, no doubt, put off by the ability of defendants, such as tobacco companies, to avoid liability because plaintiffs would never have more than evidence of risk.  In the face of relative risks often in excess of 30, with attributable risks in excess of 95%, this outcome was disturbing.

Judge Weinstein’s compromise was a pragmatic solution to the problem of adjudicating specific causation on the basis of risk evidence. Although as noted above, many scientists rejected any use of risk to support specific causation inferences, some scientists agreed with this practical solution.  Ironically, David Goldsmith, the author of the report in the Oscar Brooks case, supra, was one such writer who had embraced the relative risk cut off:

“A relative risk greater than 2.0 produces an attributable risk (sometimes called attributable risk percent10) or an attributable fraction that exceeds 50%.  An attributable risk greater than 50% also means that ‘it is more likely than not’, or, in other words, there is a greater than 50% probability that the exposure to the risk factor is associated with disease.”

David F. Goldsmith & Susan G. Rose, “Establishing Causation with Epidemiology,” in Tee L. Guidotti & Susan G. Rose, eds., Science on the Witness Stand:  Evaluating Scientific Evidence in Law, Adjudication, and Policy 57, 60 (OEM Press 2001).

In the Brooks case, Goldsmith did not have an increased risk even close to 2.0. The litigation industry ultimately would not accept anything other than full compensation for attributable risks greater than 0%.


[1] See, e.g., Sander Greenland, “Relation of the Probability of Causation to Relative Risk and Doubling Dose:  A Methodologic Error that Has Become a Social Problem,” 89 Am. J. Pub. Health 1166, 1168 (1999)(“[a]ll epidemiologic measures (such as rate ratios and rate fractions) reflect only the net impact of exposure on a population”); Joseph V. Rodricks & Susan H. Rieth, “Toxicological Risk Assessment in the Courtroom:  Are Available Methodologies Suitable for Evaluating Toxic Tort and Product Liability Claims?” 27 Regulatory Toxicol. & Pharmacol. 21, 24-25 (1998)(noting that a population risk applies to individuals only if all persons within the population are the same with respect to the influence of the risk on outcome); G. Friedman, Primer of Epidemiology 2 (2d ed. 1980)(epidemiologic studies address causes of disease in populations, not causation in individuals)