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

The New York Times Goes to War Against Generic Drug Manufacturers

March 25th, 2012

Last week marked the launch of a New York Times rhetorically fevered, legally sophomoric campaign against generic drug preemption.  Saturday saw an editorial, “A Bizarre Outcome on Generic Drugs,” New York Times (March 24, 2012), which screamed, “Bizarre”!  “Outrageous”!

The New York Times editorialists have their knickers in a knot over the inability of people, who are allegedly harmed by adverse drug reactions from generic medications, to sue the generic manufacturers.  The editorial follows a front-page article, from earlier last week, which decried the inability to sue generic drug sellers. See Katie Thomas, “Generic Drugs Proving Resistant to Damage Suits,” New York Times (Mar. 21, 2012).

The Times‘ writers think that it is “bizarre” and “outrageous” that these people are out of court due to federal preemption of state court tort laws that might have provided a remedy.

In particular, the Times suggests that the law is irrational for allowing Ms. Diana Levine to recover against Wyeth for the loss of her arm to gangrene after receiving Phenergan by intravenous push, while another plaintiff, Ms. Schork, cannot recover for a similar injury, from a generic manufacturer of promethazine, the same medication.  Wyeth v. Levine, 555 U.S. 555 (2009).  See also Brief of Petitioner Wyeth, in Wyeth v. Levine (May 2008).

Of course, both Ms. Levine and Ms. Schork received compensation from their healthcare providers, who deviated from their standard of care when they carelessly injected the medication into arteries, contrary to clear instructions.   At the time that Levine received her treatment, the Phenergan package insert contained four separate warnings about the risk of gangrene from improper injection of the medication into an artery.  For instance, the “Adverse Reactions” section of the Phenergan label indicated: “INTRA-ARTERIAL INJECTION [CAN] RESULT IN GANGRENE OF THE AFFECTED EXTREMITY.”

As any tort practitioner knows, a plaintiff can almost always second guess the adequacy of a warning, but the Levine case did not involve any real failure to warn, or failure to warn adequately.  Healthcare providers and the FDA are well aware of the dangers of intra-arterial injection of Phenergan, but plaintiffs’ counsel can always play the hindsight bias game, and ask for something more.

What distorts tort law and creates irrational, disparate outcomes often has more to do with who has the deep pocket.  The Physician’s Assistant who screwed up and caused Ms. Levine’s gangrene settled, but Ms. Levine was left free to pursue the manufacturer despite the negligence of the P.A.  According to the Times article, Ms. Schork, who was injured from a careless injection of the generic version, pursued a similar strategy, and obtained a “limited settlement” from her healthcare provider (amount not specified).

Many states have caps on medical malpractice awards, and often plaintiffs’ counsel are reluctant to push hard against healthcare providers who will be viewed as sympathetic by juries.  Punitive damages are virtually impossible to obtain against physicians, physician assistants, and nurses, but are commonplace against large pharmaceutical companies.  So tort law creates a perverse set of incentives to ignore the truly culpable parties, or “to go lightly” on them, and to pursue the manufacturing defendant on the vagaries of “adequacy” of warning. The incentives are so corrupting that in some states, the healthcare providers, afraid of insurance premium hikes, turn to plaintiffs’ counsel to offer support against the manufacturing defendant.

What is bizarre and outrageous is that anyone would have a remedy against the manufacturer in the facts of the Levine case, or in similar cases.

The Times notes that federal preemption of state tort law in lawsuits against generics follows from the generic manufacturers’ lack of control over package insert labels.  Shilling for the lawsuit industry, the Times fails to mention that it is the original sponsor of the medication, the proprietary pharmaceutical manufacturer, which prepared the new drug application, and which has had the longest experience with post-marketing surveillance of the medication.

Professor Deborah Mayo asks whether anyone is responsible for updating the label after a medication has lost its patent protection.  SeeGeneric Drugs Resistant to Lawsuits” (Mar. 22, 2012)

If the question is raised in the context of the sponsor’s continued sale of the medication, then the sponsor still must fulfill its product stewardship.  The FDA has a considerable interest in maintaining consistency across labels, and the agency has generally been reluctant to permit one manufacturer of a drug, or in a class of drugs, to deviate substantially from what the agency believes is the appropriate level of warning in the context of the indicated use.  Of course, in the case of Phenergan, none of these considerations was really involved, because the FDA was well aware of the danger of gangrene from arterial injection, and the FDA-approved label contained multiple references to the hazard.

Tort law is the wrong hobby horse to ride into battle for an equality of results.  Two patients may have equally careless healthcare providers, but one of them gets lucky and accidentally manages to inject the IV into a vein.  The other is unfortunate, and suffers grievous injury because her physician negligently injected the medication into an artery.  Tort law provides a remedy only against the careless provider whose negligence causes harm.  The other negligent provider goes free, to inflict malpractice on another victim.

Or consider two equally meritorious plaintiffs who sue two companies under almost identical facts.  One of the companies is insolvent. One of the plaintiffs will thus go uncompensated.

Or suppose we have two plaintiffs, each of whom receives a particular medication from his physician and is harmed by a well-recognized adverse drug reaction.  Although well understood by the medical professional, further assume that the harm is not adequately addressed in the package insert. One plaintiff’s physician acknowledges that he was well aware of the reaction; the other plaintiff’s physician is incompetent and says he never heard of the problem.  The learned intermediary doctrine will properly cut off the claim of the knowledgeable physician; the plaintiff whose physician was incompetent may still have a claim against the manufacturer.  Lucky plaintiff to have had such an ill-trained physician.

Similarly, if a plaintiff’s physician testifies that he never reads package inserts, then the plaintiff may lose any remedy because regardless whether the warning label was adequate or not, the prescribing physician’s carelessness prevented the actual labeling, and any hypothetical proposed labeling, from influencing his prescribing practice.

The Times quotes Michael Johnson, a lawyer who represented Gladys Mensing, whose case gave rise to the Supreme Court’s ruling on generic preemption: “Your pharmacists aren’t telling you, hey, when we fill this with your generic, you are giving up all or your legal remedies.”  Well, if you have a well-trained physician, who is aware of the risks of a medication, as he or she should be, you may have given up the rights you had if your physician was an ignoramus, willfully ignorant of the medication’s unavoidable risks.   Pliva, Inc. v. Mensing, 131 S. Ct. 2567 (2011).

The point is that tort law is not, and has never been, a system of generating equal outcomes; nor is tort law a system of insuring people against bad results.  Generic medications cost less.  People who are looking to buy lawsuits may choose to buy proprietary pharmaceutical manufacturers’ drugs.  People who are looking to buy good healthcare may choose to select their healthcare providers more carefully, and look to a system that better places the cost of harm on those parties that can best avoid the bad outcome.  In the Levine and Schork cases, the most efficient, and the fairest, cost and risk avoiders were the women’s healthcare providers.

Is it absurd for recovery to be limited against generics when allowed against proprietary pharmaceutical manufacturers?  Yes, because the proprietary manufacturers should have similar protection from the welter of state tort laws.  The irrational result in Levine is not to be imitated.

Confidence in Intervals and Diffidence in the Courts

March 4th, 2012

Next year, the Supreme Court’s Daubert decision will turn 20.  The decision, in interpreting Federal Rule of Evidence 702, dramatically changed the landscape of expert witness testimony.  Still, there are many who would turn the clock back to disabling the gatekeeping function.  In past posts, I have identified scholars, such as Erica Beecher-Monas and the late Margaret Berger, who tried to eviscerate judicial gatekeeping.  Recently a student note argued for the complete abandonment of all judicial control of expert witness testimony.  See  Note, “Admitting Doubt: A New Standard for Scientific Evidence,” 123 Harv. L. Rev. 2021 (2010)(arguing that courts should admit all relevant evidence).

One advantage that comes from requiring trial courts to serve as gatekeepers is that the expert witnesses’ reasoning is approved or disapproved in an open, transparent, and rational way.  Trial courts subject themselves to public scrutiny in a way that jury decision making does not permit.  The critics of Daubert often engage in a cynical attempt to remove all controls over expert witnesses in order to empower juries to act on their populist passions and prejudices.  When courts misinterpret statistical and scientific evidence, there is some hope of changing subsequent decisions by pointing out their errors.  Jury errors on the other hand, unless they involve determinations of issues for which there were “no evidence,” are immune to institutional criticism or correction.

Despite my whining, not all courts butcher statistical concepts.  There are many astute judges out there who see error and call it error.  Take for instance, the trial judge who was confronted with this typical argument:

“While Giles admits that a p-value of .15 is three times higher than what scientists generally consider statistically significant—that is, a p-value of .05 or lower—she maintains that this ‘‘represents 85% certainty, which meets any conceivable concept of preponderance of the evidence.’’ (Doc. 103 at 16).”

Giles v. Wyeth, Inc., 500 F.Supp. 2d 1048, 1056-57 (S.D.Ill. 2007), aff’d, 556 F.3d 596 (7th Cir. 2009).  Despite having case law cited to it (such as In re Ephedra), the trial court looked to the Reference Manual on Scientific Evidence, a resource that seems to be ignored by many federal judges, and rejected the bogus argument.  Unfortunately, the lawyers who made the bogus argument still are licensed, and at large, to incite the same error in other cases.

This business perhaps would be amenable to an empirical analysis.  An enterprising sociologist of the law could conduct some survey research on the science and math training of the federal judiciary, on whether the federal judges have read chapters of the Reference Manual before deciding cases involving statistics or science, and whether federal judges expressed the need for further education.  This survey evidence could be capped by an analysis of the prevalence of certain kinds of basic errors, such as the transpositional fallacy committed by so many judges (but decisively rejected in the Giles case).  Perhaps such an empirical analysis would advance our understanding whether we need specialty science courts.

One of the reasons that the Reference Manual on Scientific Evidence is worthy of so much critical attention is that the volume has the imprimatur of the Federal Judicial Center, and now the National Academies of Science.  Putting aside the idiosyncratic chapter by the late Professor Berger, the Manual clearly present guidance on many important issues.  To be sure, there are gaps, inconsistencies, and mistakes, but the statistics chapter should be a must-read for federal (and state) judges.

Unfortunately, the Manual has competition from lesser authors whose work obscures, misleads, and confuses important issues.  Consider an article by two would-be expert witnesses, who testify for plaintiffs, and confidently misstate the meaning of a confidence interval:

“Thus, a RR [relative risk] of 1.8 with a confidence interval of 1.3 to 2.9 could very likely represent a true RR of greater than 2.0, and as high as 2.9 in 95 out of 100 repeated trials.”

Richard W. Clapp & David Ozonoff, “Environment and Health: Vital Intersection or Contested Territory?” 30 Am. J. L. & Med. 189, 210 (2004).  This misstatement was then cited and quoted with obvious approval by Professor Beecher-Monas, in her text on scientific evidence.  Erica Beecher-Monas, Evaluating Scientific Evidence: An Interdisciplinary Framework for Intellectual Due Process 60-61 n. 17 (2007).   Beecher-Monas goes on, however, to argue that confidence interval coefficients are not the same as burdens of proof, but then implies that scientific standards of proof are different from the legal preponderance of the evidence.  She provides no citation or support for the higher burden of scientific proof:

“Some commentators have attributed the causation conundrum in the courts to the differing burdens of proof in science and law.28 In law, the civil standard of ‘more probable than not’ is often characterized as a probability greater than 50 percent.29 In science, on the other hand, the most widely used standard is a 95 percent confidence interval (corresponding to a 5 percent level of significance, or p-level).30 Both sound like probabilistic assessment. As a result, the argument goes, civil judges should not exclude scientific testimony that fails scientific validity standards because the civil legal standards are much lower. The transliteration of the ‘more probable than not’ standard of civil factfinding into a quantitative threshold of statistical evidence is misconceived. The legal and scientific standards are fundamentally different. They have different goals and different measures.  Therefore, one cannot justifiably argue that evidence failing to meet the scientific standards nonetheless should be admissible because the scientific standards are too high for preponderance determinations.”

Id. at 65.  This seems to be on the right track, although Beecher-Monas does not state clearly whether she subscribes to the notion that the burdens of proof in science and law differ.  The argument then takes a wrong turn:

“Equating confidence intervals with burdens of persuasion is simply incoherent. The goal of the scientific standard – the 95 percent confidence interval – is to avoid claiming an effect when there is none (i.e., a false positive).31

Id. at 66.   But this is crazy error; confidence intervals are not burdens of persuasion, legal or scientific.  Beecher-Monas is not, however, content to leave this alone:

“Scientists using a 95 percent confidence interval are making a prediction about the results being due to something other than chance.”

Id. at 66 (emphasis added).  Other than chance?  Well this implies causality, as well as bias and confounding, but the confidence interval, like the p-value, addresses only random or sampling error.  Beecher-Monas’s error is neither random nor scientific.  Indeed, she perpetuates the same error committed by the Fifth Circuit in a frequently cited Bendectin case, which interpreted the confidence interval as resolving questions of the role of matters “other than chance,” such as bias and confounding.  Brock v. Merrill Dow Pharmaceuticals, Inc., 874 F.2d 307, 311-12 (5th Cir. 1989)(“Fortunately, we do not have to resolve any of the above questions [as to bias and confounding], since the studies presented to us incorporate the possibility of these factors by the use of a confidence interval.”)(emphasis in original).  See, e.g., David H. Kaye, David E. Bernstein, and Jennifer L. Mnookin, The New Wigmore – A Treatise on Evidence:  Expert Evidence § 12.6.4, at 546 (2d ed. 2011) Michael O. Finkelstein, Basic Concepts of Probability and Statistics in the Law 86-87 (2009)(criticizing the overinterpretation of confidence intervals by the Brock court).

Clapp, Ozonoff, and Beecher-Monas are not alone in offering bad advice to judges who must help resolve statistical issues.  Déirdre Dwyer, a prominent scholar of expert evidence in the United Kingdom, manages to bundle up the transpositional fallacy and a misstatement of the meaning of the confidence interval into one succinct exposition:

“By convention, scientists require a 95 per cent probability that a finding is not due to chance alone. The risk ratio (e.g. ‘2.2’) represents a mean figure. The actual risk has a 95 per cent probability of lying somewhere between upper and lower limits (e.g. 2.2 ±0.3, which equals a risk somewhere between 1.9 and 2.5) (the ‘confidence interval’).”

Déirdre Dwyer, The Judicial Assessment of Expert Evidence 154-55 (Cambridge Univ. Press 2008).

Of course, Clapp, Ozonoff, Beecher-Monas, and Dwyer build upon a long tradition of academics’ giving errant advice to judges on this very issue.  See, e.g., Christopher B. Mueller, “Daubert Asks the Right Questions:  Now Appellate Courts Should Help Find the Right Answers,” 33 Seton Hall L. Rev. 987, 997 (2003)(describing the 95% confidence interval as “the range of outcomes that would be expected to occur by chance no more than five percent of the time”); Arthur H. Bryant & Alexander A. Reinert, “The Legal System’s Use of Epidemiology,” 87 Judicature 12, 19 (2003)(“The confidence interval is intended to provide a range of values within which, at a specified level of certainty, the magnitude of association lies.”) (incorrectly citing the first edition of Rothman & Greenland, Modern Epidemiology 190 (Philadelphia 1998);  John M. Conley & David W. Peterson, “The Science of Gatekeeping: The Federal Judicial Center’s New Reference Manual on Scientific Evidence,” 74 N.C.L.Rev. 1183, 1212 n.172 (1996)(“a 95% confidence interval … means that we can be 95% certain that the true population average lies within that range”).

Who has prevailed?  The statistically correct authors of the statistics chapter of the Reference Manual on Scientific Evidence, or the errant commentators?  It would be good to have some empirical evidence to help evaluate the judiciary’s competence. Here are some cases, many drawn from the Manual‘s discussions, arranged chronologically, before and after the first appearance of the Manual:

Before First Edition of the Reference Manual on Scientific Evidence:

DeLuca v. Merrell Dow Pharms., Inc., 911 F.2d 941, 948 (3d Cir. 1990)(“A 95% confidence interval is constructed with enough width so that one can be confident that it is only 5% likely that the relative risk attained would have occurred if the true parameter, i.e., the actual unknown relationship between the two studied variables, were outside the confidence interval.   If a 95% confidence interval thus contains ‘1’, or the null hypothesis, then a researcher cannot say that the results are ‘statistically significant’, that is, that the null hypothesis has been disproved at a .05 level of significance.”)(internal citations omitted)(citing in part, D. Barnes & J. Conley, Statistical Evidence in Litigation § 3.15, at 107 (1986), as defining a CI as “a limit above or below or a range around the sample mean, beyond which the true population is unlikely to fall”).

United States ex rel. Free v. Peters, 806 F. Supp. 705, 713 n.6 (N.D. Ill. 1992) (“A 99% confidence interval, for instance, is an indication that if we repeated our measurement 100 times under identical conditions, 99 times out of 100 the point estimate derived from the repeated experimentation will fall within the initial interval estimate … .”), rev’d in part, 12 F.3d 700 (7th Cir. 1993)

DeLuca v. Merrell Dow Pharms., Inc., 791 F. Supp. 1042, 1046 (D.N.J. 1992)(”A 95% confidence interval means that there is a 95% probability that the ‘true’ relative risk falls within the interval”) , aff’d, 6 F.3d 778 (3d Cir. 1993)

Turpin v. Merrell Dow Pharms., Inc., 959 F.2d 1349, 1353-54 & n.1 (6th Cir. 1992)(describing a 95% CI of 0.8 to 3.10, to mean that “random repetition of the study should produce, 95 percent of the time, a relative risk somewhere between 0.8 and 3.10”)

Hilao v. Estate of Marcos, 103 F.3d 767, 787 (9th Cir. 1996)(Rymer, J., dissenting and concurring in part).

After the first publication of the Reference Manual on Scientific Evidence:

American Library Ass’n v. United States, 201 F.Supp. 2d 401, 439 & n.11 (E.D.Pa. 2002), rev’d on other grounds, 539 U.S. 194 (2003)

SmithKline Beecham Corp. v. Apotex Corp., 247 F.Supp.2d 1011, 1037-38 (N.D. Ill. 2003)(“the probability that the true value was between 3 percent and 7 percent, that is, within two standard deviations of the mean estimate, would be 95 percent”)(also confusing attained significance probability with posterior probability: “This need not be a fatal concession, since 95 percent (i.e., a 5 percent probability that the sign of the coefficient being tested would be observed in the test even if the true value of the sign was zero) is an  arbitrary measure of statistical significance.  This is especially so when the burden of persuasion on an issue is the undemanding ‘preponderance’ standard, which  requires a confidence of only a mite over 50 percent. So recomputing Niemczyk’s estimates as significant only at the 80 or 85 percent level need not be thought to invalidate his findings.”), aff’d on other grounds, 403 F.3d 1331 (Fed. Cir. 2005)

In re Silicone Gel Breast Implants Prods. Liab. Litig, 318 F.Supp.2d 879, 897 (C.D. Cal. 2004) (interpreting a relative risk of 1.99, in a subgroup of women who had had polyurethane foam covered breast implants, with a 95% CI that ran from 0.5 to 8.0, to mean that “95 out of 100 a study of that type would yield a relative risk somewhere between on 0.5 and 8.0.  This huge margin of error associated with the PUF-specific data (ranging from a potential finding that implants make a woman 50% less likely to develop breast cancer to a potential finding that they make her 800% more likely to develop breast cancer) render those findings meaningless for purposes of proving or disproving general causation in a court of law.”)(emphasis in original)

Ortho–McNeil Pharm., Inc. v. Kali Labs., Inc., 482 F.Supp. 2d 478, 495 (D.N.J.2007)(“Therefore, a 95 percent confidence interval means that if the inventors’ mice experiment was repeated 100 times, roughly 95 percent of results would fall within the 95 percent confidence interval ranges.”)(apparently relying party’s expert witness’s report), aff’d in part, vacated in part, sub nom. Ortho McNeil Pharm., Inc. v. Teva Pharms Indus., Ltd., 344 Fed.Appx. 595 (Fed. Cir. 2009)

Eli Lilly & Co. v. Teva Pharms, USA, 2008 WL 2410420, *24 (S.D.Ind. 2008)(stating incorrectly that “95% percent of the time, the true mean value will be contained within the lower and upper limits of the confidence interval range”)

Benavidez v. City of Irving, 638 F.Supp. 2d 709, 720 (N.D. Tex. 2009)(interpreting a 90% CI to mean that “there is a 90% chance that the range surrounding the point estimate contains the truly accurate value.”)

Estate of George v. Vermont League of Cities and Towns, 993 A.2d 367, 378 n.12 (Vt. 2010)(erroneously describing a confidence interval to be a “range of values within which the results of a study sample would be likely to fall if the study were repeated numerous times”)

Correct Statements

There is no reason for any of these courts to have struggled so with the concept of statistical significance or of the confidence interval.  These concepts are well elucidated in the Reference Manual on Scientific Evidence (RMSE):

“To begin with, ‘confidence’ is a term of art. The confidence level indicates the percentage of the time that intervals from repeated samples would cover the true value. The confidence level does not express the chance that repeated estimates would fall into the confidence interval.91

* * *

According to the frequentist theory of statistics, probability statements cannot be made about population characteristics: Probability statements apply to the behavior of samples. That is why the different term ‘confidence’ is used.”

RMSE 3d at 247 (2011).

Even before the Manual, many capable authors have tried to reach the judiciary to help them learn and apply statistical concepts more confidently.  Professors Michael Finkelstein and Bruce Levin, of the Columbia University’s Law School and Mailman School of Public Health, respectively, have worked hard to educate lawyers and judges in the important concepts of statistical analyses:

“It is the confidence limits PL and PU that are random variables based on the sample data. Thus, a confidence interval (PL, PU ) is a random interval, which may or may not contain the population parameter P. The term ‘confidence’ derives from the fundamental property that, whatever the true value of P, the 95% confidence interval will contain P within its limits 95% of the time, or with 95% probability. This statement is made only with reference to the general property of confidence intervals and not to a probabilistic evaluation of its truth in any particular instance with realized values of PL and PU. “

Michael O. Finkelstein & Bruce Levin, Statistics for Lawyers at 169-70 (2d ed. 2001)

Courts have no doubt been confused to some extent between the operational definition of a confidence interval and the role of the sample point estimate as an estimator of the population parameter.  In some instances, the sample statistic may be the best estimate of the population parameter, but that estimate may be rather crummy because of the sampling error involved.  See, e.g., Kenneth J. Rothman, Sander Greenland, Timothy L. Lash, Modern Epidemiology 158 (3d ed. 2008) (“Although a single confidence interval can be much more informative than a single P-value, it is subject to the misinterpretation that values inside the interval are equally compatible with the data, and all values outside it are equally incompatible. * * *  A given confidence interval is only one of an infinite number of ranges nested within one another. Points nearer the center of these ranges are more compatible with the data than points farther away from the center.”); Nicholas P. Jewell, Statistics for Epidemiology 23 (2004)(“A popular interpretation of a confidence interval is that it provides values for the unknown population proportion that are ‘compatible’ with the observed data.  But we must be careful not to fall into the trap of assuming that each value in the interval is equally compatible.”); Charles Poole, “Confidence Intervals Exclude Nothing,” 77 Am. J. Pub. Health 492, 493 (1987)(“It would be more useful to the thoughtful reader to acknowledge the great differences that exist among the p-values corresponding to the parameter values that lie within a confidence interval … .”).

Admittedly, I have given an impressionistic account, and I have used anecdotal methods, to explore the question whether the courts have improved in their statistical assessments in the 20 years since the Supreme Court decided Daubert.  Many decisions go unreported, and perhaps many errors are cut off from the bench in the course of testimony or argument.  I personally doubt that judges exercise greater care in their comments from the bench than they do in published opinions.  Still, the quality of care exercised by the courts would be a worthy area of investigation by the Federal Judicial Center, or perhaps by other sociologists of the law.

Relative of Risk > Two in the Courts – Updated

March 3rd, 2012

See , for the updated the case law on the issue of using relative and attributable risks to satisfy plaintiff’s burden of showing, more likely than not, that an exposure or condition caused a plaintiff’s disease or injury.