In re Lipitor (Atorvastatin Calcium) Mktg., Sales Practices & Prods. Liab. Litig.

• Court: United States District Courts. 4th Circuit. United States District Court of South Carolina
• Citation: 174 F.Supp.3d 911
• Decision Date: 30 March 2016
• Docket Number: MDL No. 2:14–mn–02502–RMG
• Parties: In re: Lipitor (Atorvastatin Calcium) Marketing, Sales Practices and Products Liability Litigation

174 F.Supp.3d 911

In re: Lipitor (Atorvastatin Calcium) Marketing, Sales Practices and Products Liability Litigation

MDL No. 2:14–mn–02502–RMG

United States District Court, D. South Carolina, Charleston Division.

Signed March 30, 2016

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CASE MANAGEMENT ORDER NO. 68

This Order relates to all cases.

Richard Mark Gergel, United States District Court Judge

This matter is before the Court on Pfizer's Motion to Exclude Plaintiffs' Expert

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Testimony on the Issue of General Causation. (Dkt. No. 972). For the reasons stated below, the motion is GRANTED IN PART AND DENIED IN PART.

I. Background

In this MDL, Plaintiffs allege that Lipitor caused their Type 2 diabetes. “[I]n order to carry the burden of proving a plaintiff's injury was caused by exposure to a specified substance,” a plaintiff must demonstrate general and specific causation. Zellers v. NexTech Ne., LLC, 533 Fed.Appx. 192, 196 (4th Cir.2013)cert. denied, ––– U.S. ––––, 134 S.Ct. 911, 187 L.Ed.2d 780 (2014) ; accord Westberry v. Gislaved Gummi AB, 178 F.3d 257, 263 (4th Cir.1999). “General causation is whether a substance is capable of causing a particular injury or condition in the general population and specific causation is whether a substance caused a particular individual's injury.” Norris v. Baxter Healthcare Corp., 397 F.3d 878, 881 (10th Cir.2005). “Plaintiff[s] must first demonstrate general causation because without general causation, there can be no specific causation.” Id. Here, if Lipitor is not capable of causing diabetes, it follows that it is not the cause of diabetes in particular plaintiffs.

A. Epidemiological Method for Establishing General Causation

Epidemiology provides “the primary generally accepted methodology for demonstrating a causal relation between a chemical compound and a set of symptoms or disease.”¹ In re Meridia Products Liab. Litig., 328 F.Supp.2d 791, 800 (N.D.Ohio 2004), aff'd, 447 F.3d 861 (6th Cir.2006) ; Siharath v. Sandoz Pharm. Corp., 131 F.Supp.2d 1347, 1356 (N.D.Ga.2001), aff'd sub nom. Rider v. Sandoz Pharm. Corp., 295 F.3d 1194 (11th Cir.2002) ; Conde v. Velsicol Chem. Corp., 804 F.Supp. 972, 1025–26 (S.D.Ohio 1992), aff'd, 24 F.3d 809, 814 (6th Cir.1994).

It is well established in case law and undisputed by the parties that epidemiologists use a two-part process for determining causation. (Dkt. No. 972 at 27–28; Dkt. No. 1053 at 13); e.g., Ambrosini v. Labarraque, 101 F.3d 129, 136 (D.C.Cir.1996) ; In re Fosamax Products Liab. Litig., 645 F.Supp.2d 164, 187 (S.D.N.Y.2009) ; Giles v. Wyeth, Inc., 500 F.Supp.2d 1048, 1053 (S.D.Ill.2007). First, epidemiological studies must establish an association between exposure to a drug and a disease. (Dkt. No. 1053 at 12; Dkt. No. 972 at 27); e.g., Ambrosini, 101 F.3d at 136 ; McMunn v. Babcock & Wilcox Power Generation Grp., Inc., No. CIV.A. 10–143, 2013 WL 3487560, at *15 (W.D.Pa. July 12, 2013) ; In re Fosamax, 645 F.Supp.2d at 187 ; Be n kwith v. Matrixx Initiatives, Inc., 467 F.Supp.2d 1316, 1327 (M.D.Ala.2006) ; Soldo v. Sandoz Pharm. Corp., 244 F.Supp.2d 434, 461 (W.D.Pa.2003) ; see also Reference Manual on Scientific Evidence (RMSE) 566 (3d ed. 2011) (“[T]he first question an epidemiologist addresses is whether an association exists between exposure to the agent and disease.”). An association exists between exposure to a drug and a disease when the two “occur together more frequently than one would expect by chance.” RMSE at 566; accord

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In re Fosamax, 645 F.Supp.2d at 187 ; (accord Dkt. No. 1053 at 13). In other words, an association exists when people exposed to the drug have a higher incidence of the disease and the difference is not simply due to chance. Two common ways for evaluating whether a difference between those exposed to a drug and those not exposed could have occurred simply by chance is to calculate a p-value and to calculate the confidence interval for the relative risk ratio. RSME at 576, 580.

A p-value “represents the probability that an observed positive association could result from random error even if no association were in fact present.” Id. at 576. “To minimize false positives, epidemiologists use a convention that the p-value must fall below some selected level ... for the results of the study to be statistically significant” and, thus, establish an association. Id. The most common significance level in science is .05. Id. at 577. Thus, generally, a study's authors will only find that an association exists between a drug and a disease if the p-value is less than .05.

A second common way to evaluate whether an observed difference is due to chance is to calculate the confidence interval for the relative risk ratio. RSME at 580. The relative risk ratio is the risk of disease among people exposed to the drug divided by the risk of the disease among those not exposed to the drug. RMSE at 627. For instance, if the risk of developing diabetes while on Lipitor is 6% and the risk of developing diabetes not on Lipitor (i.e., in a placebo group) is 4%, then the relative risk of developing diabetes for Lipitor is 6/4 or 1.5. A relative risk of 1.0 indicates no difference between the two groups; the risk in the two groups is the same (e.g., 5% divided by 5% or 20% divided by 20%). A relative risk ratio above 1 indicates an increased risk in the exposed group, and a relative risk ratio less than one indicates a decreased risk in the exposed group.

A confidence interval is essentially a “margin of error” for the estimated relative risk ratio. In re Bextra & Celebrex Mktg. Sales Practices & Prod. Liab. Litig., 524 F.Supp.2d 1166, 1174 (N.D.Cal.2007). It is the “range of possible values” for the actual relative risk ratio, given the data and pre-selected level of confidence. RMSE at 580. “So, for example, if a given study showed a relative risk of 1.40 (a 40 percent increased risk of adverse events), but the 95 percent confidence interval is .8 to 1.9, we would say that we are 95 percent confident that the true value, that is, the actual relative risk, is between .8 and 1.9.” In re Bextra & Celebrex, 524 F.Supp.2d at 1174. “Because the confidence interval includes results which do not show any increased risk, and indeed, show a decreased risk, that is, it includes values less than 1.0, we would say the study does not demonstrate a ‘statistically significant’ increased risk of an adverse outcome.” Id.

Randomized, double-blind, clinical trials are the “gold standard” for determining whether an association exists. Id. at 555; (see also Singh Rep., Dkt. No. 972–6 at 6–7.) However, the Reference Manual on Scientific Evidence recognizes that observational studies can be sufficient to establish an association.² See id. at 217–18

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(“Observational studies can establish that one factor is associated with another, but work is needed to bridge the gap between association and causation.”); (see also Singh Rep., Dkt. No. 972–6 at 7 (“Absent such placebo-controlled trials to address this question, we rely on meta-analysis of randomized controlled trials to determine causation. Observational studies are often used in this setting.”)).

“Once an association has been found between exposure to an agent and development of a disease, researchers consider whether the association reflects a true cause-effect relationship.” RMSE at 597; (Dkt. No. 1053 at 14); (Dkt. No. 972 at 28); accord Ambrosini, 101 F.3d at 136McMunn, 2013 WL 3487560, at *15. In assessing causation, epidemiologists “first look for alternative explanations for the associations, such as bias or confounding factors,” and then apply the Bradford Hill factors to determine whether an association reflects a truly causal relationship. RMSE at 598–600; see also, e.g., In re Zoloft (Sertraline Hydrochloride) Products Liab. Litig., 26 F.Supp.3d 449, 454–55 (E.D.Pa.2014), recon. denied, 2015 WL 314149 (E.D.Pa. Jan. 23, 2015) ; McMunn, 2013 WL 3487560, at *15 ; Soldo, 244 F.Supp.2d at 461 ; In re Neurontin Mktg., Sales Practices, & Products Liab. Litig., 612 F.Supp.2d 116, 132 (D.Mass.2009) ; In re Fosamax, 645 F.Supp.2d at 187. These factors are (1) strength of the association, (2) replication of the findings, (3) specificity of the association, (4) temporal relationship, (5) dose-response relationship (aka biological gradient), (6) biological plausibility, (7) consistency with other knowledge (aka coherence), (8) consideration of alternative explanations, and (9) cessation of exposure.³ RMSE at 600; In re Zoloft, 26 F.Supp.3d at 454–55.

Whether an established association is causal is a matter of scientific judgment, and scientists appropriately employing this method “may come to different judgments” about whether a causal inference is appropriate. Milward v. Acuity Specialty Products Grp., Inc., 639 F.3d 11, 18 (1st Cir.2011) ; RMSE at 222, 598; see also RMSE at 552 (“Assessing whether an association is causal requires an understanding of the strengths and weaknesses of the study's design and implementation, as well as a judgment about how the study findings fit with other scientific knowledge.”). However, the authors of the Reference Manual on...