Contra Parascandola’s Reduction of Specific Causation to Risk

Mark Parascandola is a photographer who splits his time between Washington DC, and Almeria, Spain.  Before his career in photography, Parascandola studied philosophy (Cambridge), and did graduate work in epidemiology (Johns Hopkins, MPH). In 1997 to 1998, he studied the National Cancer Institute’s role in determining that smoking causes some kinds of cancer.  He went on to serve as a staff epidemiologist at NCI, at its Tobacco Control Research Branch, in the Division of Cancer Control and Population Sciences (DCCPS).

Back in the 1990s, Parascandola wrote an article, which is a snapshot and embellishment of arguments given by Sander Greenland, on the use and alleged abuse of relative risks to derive a “probability of causation.” See Mark Parascandola, “What’s Wrong with the Probability of Causation?” 39 Jurimetrics J. 29 (1998)[cited here are Parascandola]. Parascandola’s article is a locus of arguments that have recurred from time to time, and worth revisiting.

Parascandola offers an interesting historical factoid, which is a useful reminder to those who suggest that the RR > 2 argument was the brainchild of lawyers:  The argument was first suggested in 1959, by Dr. Victor P. Bond, a physician with expertise in medical physics at the Brookhaven National Laboratory.  See Parascandola at 31 n. 6 (citing Victor P. Bond, The Medical Effects of Radiation (1960), reprinted in NACCA 13th Annual Convention 1959, at 126 (1960).

Unfortunately, Parascandola is a less reliable reporter when it comes to the judicial use of the relative risk greater than two (RR > 2) argument.  He argues that Judge Jack Weinstein opposed the RR > 2 argument on policy grounds, when in fact, Judge Weinstein rejected the anti-probabilistic argument that probabilistic inference could never establish specific causation, and embraced the RR > 2 argument as a logical policy compromise that would allow evidence of risk to substitute for specific causation in a limited fashion. Parascandola at 33-34 & n.20. Given Judge Weinstein’s many important contributions to tort and procedural law, and the importance of the Agent Orange litigation, it is worth describing Judge Weinstein’s views accurately. See In re Agent Orange Product Liab. Litig., 597 F. Supp. 740, 785, 817, 836 (E.D.N.Y. 1984) (“A government administrative agency may regulate or prohibit the use of toxic substances through rulemaking, despite a very low probability of any causal relationship.  A court, in contrast, must observe the tort law requirement that a plaintiff establish a probability of more than 50% that the defendant’s action injured him. … This means that at least a two-fold increase in incidence of the disease attributable to Agent Orange exposure is required to permit recovery if epidemiological studies alone are relied upon.”), aff’d 818 F.2d 145, 150-51 (2d Cir. 1987)(approving district court’s analysis), cert. denied sub nom. Pinkney v. Dow Chemical Co., 487 U.S. 1234 (1988); see also In re “Agent Orange” Prod. Liab. Litig., 611 F. Supp. 1223, 1240, 1262 (E.D.N.Y. 1985)(excluding plaintiffs’ expert witnesses), aff’d, 818 F.2d 187 (2d Cir. 1987), cert. denied, 487 U.S. 1234 (1988).[1]

Parascandola’s failure to cite and describe Judge Weinstein’s views raises some question of the credibility of his analyses, and his assertion that “[he] will demonstrate that the PC formula is invalid in many situations and cannot fill the role it is given.” Parascandola at 30 (emphasis added).

Parascandola describes basic arithmetic of probability of causation (PC) in terms of a disease for which we “expect cases” and for which we have “excess cases.” The rate of observed cases in an exposed population divided by the rate of expected cases in an unexposed population provides an estimate of the population relative risk (RR). The excess cases can be obtained simply from the difference between observed cases in the exposed group and the expected cases in the unexposed group.  The attributable fraction is the ratio of excess cases to total cases.

The probability of causation “PC” = 1 – (1/RR).

Heterogeneity Yields Uncertainty Argument

The RR describes a group statistic, and an individual’s altered risk will almost certainly not be exactly equal to the group’s average risk. Parascandola notes that sometimes this level of uncertainty can be remedied by risk measurements for subgroups that better fit an individual plaintiff’s characteristics.  All true, but this is hardly an argument against RR > 2.  At best, the heterogeneity argument is an expression of inference skepticism of the sort that led Judge Weinstein to accept RR > 2 as a reasonable compromise. The presence of heterogeneity of this sort simply increases the burden upon plaintiff to provide RR statistics from studies that very tightly resemble plaintiff in terms of exposure and other characteristics.

Urning for Probablistic Certainty

Parascandola describes how the PC formula arises from a consideration of the “urn model” of disease causation.  Suppose in group of sufficient size there were expected 200 stomach cancer cases within a certain time, but 300 were observed. We can model the situation with an urn of 300 marbles, 200 of which are green, and 100 are red. Blindfolded or colorblind, we pull a single marble from the urn, and we have only a 1/3 chance of obtaining a red, “excess” marble case. Parascandola at 36-37 (borrowing from David Kaye, “The Limits of the Preponderance of the Evidence Standard: Justifiably Naked Statistical Evidence and Multiple Causation,” 7 Am. Bar Fdtn. Res. J. 487, 501 (1982)).

Parascandola argues that the urn model is not necessarily correct.  Causation cannot always be reduced to a single cause. Complex etiologic mechanisms and pathways are common.  Interactions between and among causes frequently occur.  Biological phenomena are sometimes “over-determined.” Parascandola asks us to assume that some of the non-excess cases are also “etiologic cases,” which were caused by the exposure but which would not have occurred but for the exposure.  Id. at 37. Borrowing from Greenland, Parascandola asserts that “[a]ll excess cases are etiologic cases, but not vice versa.” Id. at 38 & n.37 (quoting from Sander Greenland & James M. Robins, “Conceptual Problems in the Definition and Interpretation of Attributable Fractions,” 128 Am. J. Epidem. 1185, 1185 (1988)).

Parascandola’s argument, if accepted, proves too much to help plaintiffs who hope to establish specific causation with evidence of increased risk. His argument posits a different, more complex model of causation, for which plaintiffs usually have no evidence.  (If they did have such evidence, then they would have nothing to fear in the assumptions of the simplistic urn model; they could rebut those assumptions.) Parascandola’s argument pushes the speculation envelope by asking us to believe that some “non-excess” cases are etiologic cases, but providing no basis for identifying which ones they are.  Unless and until such evidence is forthcoming, Parascandola’s argument is simply uncontrolled multi-leveled conjecture.

Again borrowing from Sander Greenland’s speculation, Parascandola advances a variant of the argument above by suggesting that an exposure may not increase the overall number of excess cases, but that it may accelerate the onset of the harm in question. While it is true that the element of time is important, both in law and in life, the invoked speculation can be, and usually is, tested by time windows or time series analyses in observational epidemiology and clinical trials.  The urn model is “flat” with respect to the temporal dimension, but if plaintiffs want to claim acceleration, then they should adduce Kaplan-Meier curves and the like.  But again, with the modification of the time dimension, plaintiffs will still need hazard ratios or other risk ratios greater than two to make out their case, unless there is a biomarker/fingerprint of individual causation. The introduction of the temporal element is important to an understanding of risk, but Parascandola’s argument does not help transmute evidence of risk in a group to causation in an individual.

Joint Chancy Causation

In line with his other speculative arguments, Parascandola asks:  what if a given cancer in the exposed group is the product of two causes rather than due to one or another of the two causes? Parascandola at 40. This question restates the speculative argument in only slightly different terms.  We could multiply the possible causal sets by suggesting that the observed effect resulted from one or the other or both or none of the causes.  Parascandola calls this “joint chancy causation,” but he manages to show only that the inference of causation from prior chance or risk is a very chancy (or dicey) step in his argument.  Parascandola argues that we should not assume that the urn model is true, when multiple causation models are “plausible and consistent” with other causal theories.

Interestingly, this line of argument would raise the burden upon plaintiffs by requiring them to specify the applicable causal model in ways that (1) they often cannot, and (2) they now, under current law, are not required to do.

Conclusion

In the end, Parascandola realizes that he has raised, not lowered, the burden for plaintiffs.  His counter is to suggest, contrary to law and science, that “the existence of alternative hypotheses should not prevent the plaintiff’s case from proceeding.” Parascandola at 41 n.50.  Because he says so. In other words, Parascandola is telling us that irrespective of how poorly established a hypothesis is, or of how speculative an inference is, or of the existence and strength of alternative hypotheses,

“This trial must be tried.”

W.S. Gilbert, Trial by Jury (1875).

With bias of every kind, no doubt.

That is not science, law, or justice.


[1] An interesting failure or lack of peer review in a legal journal.

 

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