G.S. et al v. PFIZER, INC. et al

Filing 8

MEMORANDUM AND/OR OPINION. SIGNED BY HONORABLE CYNTHIA M. RUFE ON 12/2/2015. 12/3/2015 ENTERED AND COPIES E-MAILED TO LIAISON COUNSEL. (SEE PAPER # 1519 IN 12-MD-2342) (ems)

IN THE UNITED STATES DISTRICT COURT
FOR THE EASTERN DISTRICT OF PENNSYLVANIA
:
IN RE: ZOLOFT (SERTRALINE
:
HYDROCLORIDE) PRODUCTS
:
LIABILITY LITIGATION
:
_______________________________________:
:
THIS DOCUMENT APPLIES TO:
:
:
See Appendix A
:

MDL No. 2342
12-md-2342

MEMORANDUM OPINION
Rufe, J.

December 2, 2015

The PSC seeks to offer Nicholas Jewell, Ph.D. as a general causation expert in the Zoloft
MDL (MDL 2342). Dr. Jewell’s opinion is summarized as follows: “I conclude that maternal use
of Zoloft during early pregnancy is capable of causing, or contributing to cause, cardiovascular
birth defects.” Pfizer, the maker of Zoloft, challenges the admissibility of this evidence, asserting
that Dr. Jewell has used unreliable methods to form his opinion that Zoloft, when taken early in
pregnancy, may cause cardiovascular birth defects. Presently before the Court is Pfizer’s Motion
to Exclude the Testimony of Plaintiffs’ Steering Committee (“PSC”) Expert Witness Nicholas
Jewell, Ph.D. The Court has reviewed Dr. Jewell’s expert report, the rebuttal reports, and the
parties’ briefs, and heard evidence and argument at a four-day Daubert hearing.
I.

BACKGROUND
Zoloft is a prescription antidepressant, commonly used to treat depression, anxiety, and

other mental health conditions. The active ingredient in Zoloft is sertraline. Zoloft is one of a
class of drugs known as selective serotonin reuptake inhibitors (SSRIs). Serotonin is a
neurotransmitter produced endogenously by humans and other animals. The SSRIs do not

contain serotonin; rather, they alter the availability in the nervous system of the serotonin
produced by the body. The FDA categorizes Zoloft as a Pregnancy Category C drug. 1
The parties agree that birth defects, including the cardiac birth defects alleged in this litigation,
have occurred throughout history. Major congenital heart defects, which are among the most
prevalent birth defects, occur in as many as 1% of live births. Expanding the scope to include all
cardiac defects, one finds an incidence of approximately 7.5% of live births. Although some
birth defects are caused by known genetic sources or environmental agents (such as certain
viruses, radiation exposure, or teratogenic medications), most are due to currently unknown
causes. Teratology is the scientific field which deals with the cause and prevention of birth
defects.
Where plaintiffs allege that a medication, such as Zoloft, is a teratogen, it is common to
put forth experts whose opinions are based upon epidemiological evidence. Although the “gold
standard” for epidemiological studies is the double-blind, randomized control trial, such studies
may not ethically be conducted on pregnant women. Therefore, the research on teratogens relies
upon less rigorous observational studies. This does not mean that inferences about causation
cannot be made; it simply means that the researchers (and the litigation expert) must more
carefully examine the power of the study to detect associations, the role of chance, and any

1

The FDA has established 5 categories to indicate the potential of a drug to cause birth defects if used
during pregnancy. Category A means that there are adequate, well-controlled studies which have failed to
demonstrate a risk to the fetus. Few drugs are in category A because controlled studies of medication use during
pregnancy are ethically prohibited. Category B means animal studies show no risk, but there are no adequate and
well-controlled studies of use by pregnant women. Category C means that animal reproduction studies have shown
an adverse effect on the fetus, but there are no adequate and well-controlled studies in humans, and so pregnant
women should weigh the potential benefits against the potential risks. Category D is used when there is positive
evidence of human fetal risk based on adverse reaction data from investigational or marketing experience or studies
in humans, but potential benefits may still warrant use of the drug. Category X is the lowest category, used when use
of the drug is not recommended for any pregnant women, as the risks clearly outweigh any benefits. One SSRI,
Paxil, is a Category D drug, while all other SSRIs, including Zoloft, are Category C drugs.

2

possible sources of bias or confounding which may make the study a weak indicator of
causation.
The best observational studies are designed and powered to test the outcome of interest
(here, cardiovascular birth defects). 2 For rare defects, it is necessary to include very large
samples of women in both the exposed and the unexposed groups, or one will not be able to
reliably measure any true increase in risk that exists. Where individual studies are underpowered
to detect rare outcomes, studies can sometimes be combined using a meta-analysis, to increase
the power to detect increases in risk.
Epidemiological studies examining the effects of medication taken during pregnancy on
birth defects calculate a relative risk or odds ratio (the Court will use the term “odds ratio” in this
opinion). Simply speaking, the odds ratio is calculated by dividing the odds of a particular birth
defect occurring in children who were exposed to a medication in utero by the risk or odds of
finding that birth defect in children born without prenatal exposure. Where the incidence of birth
defects is approximately the same in medication-exposed and unexposed women, the odds ratio
will be close to one. The odds ratio is interpreted as the increase in the risk of the outcome of
interest (here, cardiac birth defect) associated with the exposure of interest (Zoloft) that is above
and beyond the baseline risk.
Researchers often statistically control for certain suspected and measurable confounding
factors (e.g., factors such as maternal age, weight, smoking, alcohol use, folic acid use, etc.,
which are correlated with exposure to the medication, and which may themselves contribute to
an increased risk of the birth defect at issue), when information about those factors is available in
the data set. When this is done, the researchers will report an “adjusted” odds ratio. The authors

2

In re Diet Drugs Prods. Liab. Litig., MDL No. 1203, 2001 WL 454586 at *13 (E.D. Pa. Feb. 1, 2001).

3

of the studies Dr. Jewell has reviewed rely upon adjusted odds ratios, where available, when
drawing conclusions.
Because an odds ratio calculation is only an estimate, the precision of which may be
affected by general or study-specific factors (including confounders and biases, sample sizes,
study methods, etc.), researchers also use statistical formulas to calculate a 95% confidence
interval (sometimes abbreviated as CI), which is an estimated range of plausible odds ratio
values. A 95% confidence interval means that there is a 95% chance that the “true” odds ratio
value falls within the confidence interval range. Some confidence intervals are narrow, indicating
that the calculated rate ratio is fairly precise, and some are wide, indicating that it is not and that
additional research is warranted. If the lower bound of the confidence interval is greater than
one, researchers say that the ratio is “statistically significant” (i.e., there is only a 5% chance that
the increased risk reflected in the odds ratio is the result of chance alone), and will report finding
a statistically significant correlation or association between the medication exposure and the
birth defect at issue. 3 A statistically significant result does not necessarily indicate a large
increase in risk; it simply indicates that the increased risk found is unlikely to result from chance
alone.
Even where the confidence interval is narrow and the increased risk is statistically
significant, scientists will not draw firm conclusions from a single study, as apparent associations
may reflect random error, bias, confounding, or some weakness in the study design, or they may
be incongruous with existing scientific knowledge about biological mechanisms. When specific
design flaws or potential confounders or biases are identified, researchers will attempt to design
studies in such a way that they can determine the degree to which those factors contributed to an
3
A factor may also be protective. For example, prenatal exposure to folic acid is associated with a decrease
in neural tube defects. If a factor is protective, the ratio estimate will be less than one, and if the confidence
interval’s upper bound is less than one, that protection is statistically significant.

4

outcome. In general, before concluding that there is a “true” association between maternal
medication use and birth defects, the teratology community requires repeated, consistent,
statistically significant human epidemiological findings, and studies which address suspected
confounders and biases. 4
Epidemiological studies alone can only inform scientists that two events (e.g., medication
exposure and a birth defect) are associated. For this litigation, Dr. Jewell has been asked to opine
as to whether Zoloft causes the birth defects at issue, which requires analysis beyond the
identification of statistical correlations reported in published epidemiological studies. To infer a
causal relationship from an association, scientists look at well-established factors sometimes
referred to as the Bradford-Hill criteria. These include: the strength of the association between
the exposure and the outcome; the temporal relationship between the exposure and the outcome;
the dose-response relationship; replication of findings; the biological plausibility of such an
association; alternative explanations for the association; the specificity of the association (i.e.,
does an outcome have only one cause, or several); and the consistency with other scientific
knowledge.
Pfizer’s challenge to Dr. Jewell’s testimony focuses on the methods Dr. Jewell used to
determine whether there is a true association between maternal Zoloft use and cardiac birth
defects, his assessment of the replicability and consistency of study results, and his efforts to
address alternative explanations (chance, bias, confounding) for detected associations and the
specificity of the association.

4

“Absent consistent, repeated human epidemiological studies showing a statistically significant increased
risk of particular birth defects associated with exposure to a specific agent, the community of teratologists does not
conclude that the agent is a human teratogen.” Wade-Greaux v. Whitehall Labs., Inc., 874 F. Supp. 1441, 1453
(D.V.I. 1994), aff’d 46 F.3d 1120 (3d Cir. 1994).

5

II.

STANDARD OF REVIEW
Federal Rule of Evidence 702 reads:
[I]f scientific, technical or other specialized knowledge will assist
the trier of fact to understand the evidence or to determine a fact in
issue, a witness qualified as an expert by knowledge, skill,
experience, training or education, may testify thereto in the form of
an opinion or otherwise, if (1) the testimony is based upon
sufficient fact or data, (2) the testimony is the product of reliable
principles and methods; and (3) the witness has applied the
principles and methods reliably to the facts.

The Third Circuit has distilled this rule to two essential inquiries: 1) is the proffered expert
qualified to express an expert opinion; and 2) is the expert opinion reliable? 5 In this case, Pfizer
primarily challenges the reliability of the opinions.
Under the Third Circuit framework, the focus of the Court’s inquiry must be on the
experts’ methods, not their conclusions. Therefore, the fact that Plaintiffs’ experts and
defendants’ experts reach different conclusions does not factor into the Court’s assessment of the
reliability of their methods. 6 The experts must use good grounds to reach their conclusions, but
not necessarily the best grounds or unflawed methods. 7 Expert evidence must be relevant and
reliable to be admissible. The Court must consider: 1) whether the expert’s theory can be tested;
2) whether studies have been subject to peer review and publication; 3) the potential for error in
a technique used; and 4) the degree to which a technique or theory (but not necessarily a
conclusion) is generally accepted in the scientific community. 8

5

In re TMI Litig., 193 F.3d 613, 664 (3d Cir. 1999).

6

However, where the scientific community considers the evidence to be inconclusive, a difference of
opinion may sometimes undermine the reliability of an expert=s conclusion that there is a causal link, and may
justify excluding that expert. Magistrini v. One Hour Martinizing Dry Cleaning, 180 F. Supp. 2d 584, 607 (D.N.J.
2002), aff=d 68 F. App=x 356 (3d Cir. 2003).
7

In re Paoli R.R. Yard PCB Litig., 35 F.3d 717, 745 (3d Cir. 1994); Holbrook v. Lykes Bros. S.S. Co., 80
F.3d 777, 784 (3d Cir. 1996).
8

Daubert v. Merrell Dow Pharma., Inc., 509 U.S. 579, 593-94 (1993).

6

The Court must also consider Rule 403, which provides that “[t]he court may exclude
relevant evidence if its probative value is substantially outweighed by a danger of one or more of
the following: unfair prejudice, confusing the issues, [or] misleading the jury.”
III.

DISCUSSION
Dr. Jewell has asserted that it is his opinion, supported by a reasonable degree of

scientific certainty, that maternal use of Zoloft during the first trimester of pregnancy can cause
cardiac defects in newborns. In his testimony, Dr. Jewell noted that the research on this issue has
produced equivocal results; this, he explained, is why experts can be helpful to the jury as it
assesses the scientific literature.
Dr. Jewell’s report includes a detailed review of 11 published, peer-reviewed
epidemiological studies which he opines are the “core studies” examining whether the use of
SSRIs during pregnancy are associated with an increased risk in birth defects and which
provided Zoloft-specific cardiovascular birth defects odds ratios or relative risk ratios. 9 Dr.
Jewell noted that several studies found a statistically significant association between Zoloft use
during pregnancy and cardiac birth defects, but subsequent studies suggested that those
associations might be the result of confounding by indication 10 or an unknown confounding
factor. Dr. Jewell reviewed peer-reviewed, published studies which were designed to examine
the issue of confounding by indication, but also performed his own re-analysis of some of the
9

Dr. Jewell focused on the following studies: Ban (2014); Huybrechts (2014); Jimenez-Solem (2012);
Colvin (2011); Malm (2011); Kornum (2010); Reis-Kallen (2010); Pederson (2009); Alwan (2007); Louik (2007);
and Kallen (2007). In addition to these core studies, he reviewed additional studies and meta-analyses, including
meta-analyses by Myles (2013) and McDonagh (2014) which are peer-reviewed, published studies but which do not
contain original data.
10

Researchers acknowledge that those who are prescribed a medication may be inherently or systematically
different from those who do not take a medication, by virtue of having the medical condition the drug is prescribed
to treat (i.e., the “indication” for the medication), and these differences, not the drug itself, may account for the
increased risk for the outcomes of interest. For example, maternal depression may itself increase the risk of certain
health outcomes in children, or it may be associated with other behaviors which are related to health outcomes (e.g.
smoking, obesity). This concept is referred to as confounding by indication.

7

data from the Jimenez-Solem (2012) and Huybrechts (2014) studies (alone and in combination).
Dr. Jewell’s reanalysis is discussed in his expert report, but it has not been subject to peer review
or been published.
Pfizer does not challenge Dr. Jewell’s expertise in statistics, but does challenge Dr.
Jewell’s qualifications to make certain assumptions about embryological development, heart
defects, and categories of antidepressant medications, as it argues that he does not have the
necessary expertise to make assumptions in these areas.
Pfizer also argues that Dr. Jewell has employed flawed methodology in forming his
opinion, and thus has formed an opinion which is not shared by the relevant scientific
community, and which is not supported by the published research. Specifically, Pfizer contends,
inter alia, that: 1) Dr. Jewell relies upon data from overlapping study populations in asserting
that there are replicated, statistically significant studies demonstrating an increase in risk; 2) Dr.
Jewell’s opinion improperly relies on observed trends in data, and downplays the importance of
statistical significance; and 3) Dr. Jewell employs inconsistent standards when reviewing and
relying upon studies and types of studies, depending upon whether or not they support his a
priori opinion. Finally, Pfizer argues that, in assessing the role of confounding factors, Dr. Jewell
improperly conducted post-hoc re-analyses and a meta-analysis of certain data. The Court will
address each argument in turn.
A.

Qualifications to Make Certain Assumptions

Dr. Jewell holds a doctorate degree in mathematics from the University of Edinburgh,
Scotland, and he has been a professor of biostatistics at the University of California, Berkeley for
more than 30 years. He has authored a textbook entitled Statistics for Epidemiology, has
published hundreds of peer reviewed papers on biostatistics, and has served as an expert witness

8

in several pharmaceutical mass tort cases. Pfizer does not challenge Dr. Jewell’s expertise in
biostatistics.
However, with regard to his opinion in this case, Pfizer challenges Dr. Jewell’s
qualifications to make certain assumptions about embryological development and the etiology of
heart defects, specifically challenging his assumptions that it is appropriate to group certain heart
defects in epidemiological studies. While the Court (and Dr. Jewell himself) agrees with Pfizer
that Dr. Jewell is not an expert in these areas, the record indicates that, for the most part, Dr.
Jewell’s opinion does not rely upon assumptions he has independently reached regarding these
issues, but rather is based upon the assumptions, definitions, and classifications made by the
epidemiological study authors. 11
For example, Dr. Jewell examined cardiovascular outcomes as they were grouped by
study investigators, and “did not independently modify or pool the study endpoints.” 12 Dr. Jewell
noted:
[the] statistical trade-off when grouping outcome categories for the purpose of reporting
and analyses. If an exposure such as Zoloft only increases the risk of some of the
outcomes in an outcome category, then looking at the whole category will understate the
adverse effect of Zoloft. . . On the other hand, with very fine categorizations, one is left
with very few defects to assess in a rare sub-category. This means that there will be very
little statistical power to definitively address an association with an exposure. 13
Because of his understanding of the potential benefits and drawbacks of grouping outcomes, Dr.
Jewell looked at the data and results for broader categories first (e.g. all cardiac defects), and
then for narrower categories and individual defects when the study authors provided information
on subcategories. However, in all instances, he used the outcome categories determined by the
study authors, and Dr. Jewell clearly considered the benefits and drawbacks of looking at both
11

An exception will be discussed in Section D, below.

12

Report at 17.

13

Report at 18.

9

broad and narrow categories of injuries. Because Dr. Jewell’s opinion does not depend on his
making independent assumptions about how to categorize cardiac defects, the Court concludes
that he was not making assumptions outside of his area of expertise and finds no fault with his
methodology in this regard. 14
B.

Interpretation of the Relevant Research

1.

No Replicated Statistically Significant Findings from Non-Overlapping Data

The Court has previously noted the importance of replicated, statistically significant
findings in the field of teratology, noting that scientists “who are examining potential teratogens
generally will not draw causal conclusions in the absence of replicated statistically significant
epidemiological findings . . . .” 15
Dr. Jewell acknowledges the importance of replication of statistically significant results.
For example, he noted that random error may account for statistically significant associations
(i.e. produce a false positive) when researchers have performed multiple comparisons within the
same study, as in most of the core studies regarding SSRIs and birth defects. Although there are
statistical methods for correcting for multiple comparisons within a single study (e.g. the
Bonferroni correction), these methods are very conservative. Therefore, Dr. Jewell wrote that
the preferred method to assess random variation is to look for replication of risk across
different studies and different populations. If similar results for a given association (here,
maternal Zoloft use and cardiovascular birth defects) are reported across multiple studies,
the probability that a spurious [statistically significant] finding in one study is repeated by
chance in another is very low. 16

14

Similarly, in examining confounding by indication, Dr. Jewell assumes that users of SSRI medications
and non-SSRI antidepressants are all being treated for depression. While this assumption may be subject to
challenge, Dr. Jewell merely adopts this assumption from the study authors, and does not purport to have the
expertise to arrive at it independently.
15

Memorandum Opinion and Order dated January 23, 2015 at 3; see also Memorandum Opinion and Order
dated June 27, 2014.
16

Report at 6-7.

10

Although Dr. Jewell articulates the importance of replication of statistically significant
results across studies and independent populations, Pfizer argues that Dr. Jewell’s opinion is
unreliable because it is not, in fact, supported by replicated, statistically significant findings from
studies using non-overlapping (independent) populations.
The Court acknowledges that the relevant literature did, a year ago, include some
replication of statistically significant results for cardiac defects, which is one reason the Court
allowed the PSC to submit a new expert report focusing on cardiac claims. Specifically, a
statistically significant positive association between Zoloft use and “all cardiac defects” was
reported by both Kornum (2010) and Jimenez-Solem (2012). Louik (2007), Pederson (2009),
Kornum (2010), and Jimenez-Solem (2012) all reported statistically significant positive
associations between Zoloft use and septal defects. A recent study, Bérard (2015), has also
reported a statistically significant association between Zoloft use and certain septal defects.
However, Pfizer argues that there is no true replication of results because: 1) Pederson
(2009), Kornum (2010), and Jimenez-Solem (2012) all use overlapping data from the Danish
population; 2) the Bérard (2015) study is unreliable; and 3) the Louik (2007) study was corrected
and no longer reports a statistically significant association between Zoloft use and septal
defects. 17
With regard to the Danish population studies, Pfizer argues that Pederson (2009),
Kornum (2010), and Jimenez-Solem (2012) cannot be treated as replications because they use
overlapping data from the Danish population. Moreover, Pfizer argues, the data from all three
studies is included in a much larger study by Furu (2015), which did not replicate the results of

17

Pfizer also argues that one of the Danish studies Dr. Jewell relies upon, Jimenez-Solem (2012), concluded that the
statistically significant results it obtained for Zoloft were the result of confounding, and did not reflect a true
increase in risk associated with Zoloft use. The Court addresses Dr. Jewell’s approach to the issue of confounding
later in this opinion.

11

the earlier studies, indicating that some unknown factor, confounder, or bias may have been at
play in the earlier studies (as Jimenez-Solem (2012) concluded).
In his report and at the Daubert hearing, Dr. Jewell addressed the issue of overlapping
study populations, noting that when studies are not completely overlapping in time or population,
“the studies in fact provide important evidence of replication of findings. . .” 18 However, Pfizer
pointed out at the Daubert hearing that the overlap between the studies may be more extensive
than Dr. Jewell understood when he was preparing his report, 19 and thus the value of later studies
as replications for earlier studies may be diminished. 20
Shortly after Dr. Jewell produced his expert report (which is dated March 6, 2015), the
British Medical Journal published Furu (2015). Furu (2015) is a large study, including 7,245
babies exposed to Zoloft in the first trimester. The study population overlapped almost
completely with the earlier Danish studies, but also incorporated thousands of additional subjects
from other countries. 21 According to Dr. Jewell’s own explanation of the replication value of
partially overlapping studies, Furu (2015) should provide important information about the
reliability of the odds ratios found in the earlier studies.
Despite including virtually all the data from the earlier Danish studies, Furu (2015) did
not replicate the findings from those studies. The earlier Danish studies reported that the risk of
all cardiac defects and septal defects was approximately tripled in the exposed group (i.e.
reported odds ratios at or near 3.0). In contrast, the Furu (2015) study authors found an adjusted
18

Report at 23.

19

For example, Dr. Jewell learned at the Daubert hearing that Zoloft was not approved for use in Denmark
during the early years of the Kornum (2010) study. Therefore, with regard to Zoloft, only two years of data from
one county in Denmark used by Kornun (2010) was not included in later Danish population studies.
20

See Doc. No. 1483, Tr. at 3-21.

21
Jimenez-Solem (2012) and Pederson (2009) are complete subsets of Furu, and Kornum (2010) contained
an additional two years worth of data from a single county. In addition to the overlapping data from Denmark, Furu
(2015) included data from other countries, and studied a total of 2.3 million pregnancies. Doc. No. 1483, Tr. at 21.

12

odds ratio of 1.13 (with a narrow CI of .93-1.38, indicating that 1.13 is a fairly precise estimate)
for Zoloft and any cardiac defects, and 1.05 (CI of .82-1.35) for Zoloft and septal defects,
indicating that there is no association between Zoloft use and cardiac birth defects.
Dr. Jewell testified that he was familiar with Furu (2015), although it was published after
his report was prepared. However, he failed to provide any methodological or statistical
explanation for why this larger, later study failed to replicate the findings of the earlier studies, or
why the earlier studies should be considered more reliable than Furu (2015). Scientists are
expected to address and reconcile data that does not support their opinions, and not simply rely
upon data which does. Was Furu (2015) better designed, addressing design flaws, biases, and
confounders which inflated associations reported in earlier studies, 22 or was it poorly designed
and thus unable to detect a true association? Dr. Jewell fails to explain his reasoning, leaving the
Court to speculate as to why Dr. Jewell continues to rely upon the earlier Danish studies
notwithstanding the very different results of Furu (2015).
Assuming, arguendo, that the earlier Danish studies contain reliable measures of
increased risk, the Court then turns to the question of whether any studies using independent data
sets have replicated the results of the earlier Danish studies. It is not clear the extent to which Dr.
Jewell relies upon Bérard (2015) for his conclusion that there are independent, replicated,
statistically significant findings regarding an association between Zoloft exposure and congenital
septal defects, as Bérard (2015) was not yet published at the time Dr. Jewell submitted his report.
Nevertheless, as Dr. Jewell’s testimony revealed that he is familiar with the study, and as one
odds ratio from the Bérard (2015) study was included on his chart illustrating the data, the Court
will address Pfizer’s argument regarding the study’s reliability.
22

For example, Furu (2015), unlike the earlier Danish studies, included a sibling analysis, using data from
women who had had multiple pregnancies.

13

Typically, the Court would find a statistician’s reliance on the results of a peer-reviewed,
published epidemiological study to be methodologically sound. However, in this instance,
Pfizer’s expert (Dr. Kimmel), Dr. Jewell, and at least one member of the PSC’s legal team all
tried and failed to duplicate the statistically significant results reported by Bérard (2015), using
the data published by the study authors, even though they were able to duplicate all the nonsignificant results she reported. As Pfizer pointed out, this calls into question the reliability of the
results reported as statistically significant in the publication.
Dr. Jewell initially testified that he believed the Bérard (2015) results were reliable,
despite the fact that he could not duplicate the result with the data reported in the publication,
based upon Dr. Bérard’s explanation that she had corrected for multiple pregnancies using data
not publicly available, and her assurances that her results were correct. However, upon further
questioning by Pfizer’s counsel, Dr. Jewell testified that he had “no way of checking. . . if Dr.
Bérard is either telling the truth or. . . whether she did the analysis correctly from raw data.” 23 On
cross examination, Dr. Jewell acknowledged that a 2013 abstract for the Bérard (2015) paper
reported preliminary results, all non-significant, which matched the results Dr. Kimmel, Dr.
Jewell, and the PSC produced when they ran the statistical analysis, and the abstract stated that
the authors had arrived at those results by utilizing a statistical model which adjusted for multiple
pregnancies. 24 After reviewing this abstract during cross examination, Dr. Jewell noted that it did
not appear that a subsequent analysis to adjust for multiple pregnancies accounted for the
difference between the results reported in the 2013 abstract and the 2015 paper, and testified that
he was unable to explain, or even speculate as to, why there was a difference between the results

23

Doc. No. 1483, Tr. at 52.

24

Doc. No. 1483, Tr. at 53.

14

reported in the abstract and those reported in the published study. 25 Because, upon crossexamination, Dr. Jewell expressed a lack of confidence in the reliability of the Bérard (2015)
paper’s reported statistically significant findings, the Court will not allow Dr. Jewell to rely upon
this study as evidence of replication of statistically significant findings.
Finally, at the time Dr. Jewell submitted his report, Louik (2007) (which used data from
American women) reported a statistically significant association between Zoloft use and septal
defects. As originally published, Louik (2007) reported a statistically significant doubling of risk
of septal defect (odds ratio of 2.0, with a confidence interval of 1.2-4.0). As a result of an inquiry
by Pfizer’s expert, the study authors discovered a transcription error that required correction of
the originally reported confidence interval. The publisher of the paper, the New England Journal
of Medicine, has published a correction. The confidence interval is now reported as 1.0-4.0,
which is not statistically significant. The highly respected New England Journal of Medicine
believed this correction to be meaningful, and the journal required the authors to modify their
discussion to reflect this change in the reported confidence interval. 26
Because the Louik (2007) confidence interval remains on the borderline of being
statistically significant, and because it is just one of multiple studies, Dr. Jewell testified that this
correction did not change his opinion. Dr. Jewell explained: “if we were here and [Louik] was
the only study that we had information on regarding Zoloft and cardiac defects and in specific,

25

Doc. No. 1483, Tr. at 53.

26

Dr. Jewell suggested that certain email correspondence between professionals, which was presented to
the Court at the Daubert hearing, indicates that other scientists believe that it makes no meaningful difference
whether the lower bound of the relevant confidence interval is 1.0 (which is not statistically significant) or 1.2 (the
originally reported lower bound of the confidence interval). See Doc. No. 1483, Tr. at 33. However, this
correspondence could also be read as addressing a different point: does it matter whether the lower bound to the
confidence interval was rounded up or down (e.g. up from .99 or down from 1.01 to 1.0). See Doc. No. 1481, Tr. at
155-156. The Court’s analysis does not require it to adopt one of these interpretations, as statistical significance
retains its traditional importance according to a leading scientific publication, but is not the only factor the Court
considers.

15

septal, that [change in the confidence interval] becomes much more important. I think it becomes
important to the Court. It becomes important to the statistician. . . . [B]ut that’s not the context I
was in, in July. I was in the context where [Louik] was one of a dozen or so studies. . . . It’s a
tiny change in a sea of evidence.” 27 Because the odds ratio Louik (2007) reported for septal
defects (2.0) was consistent with findings from other studies, and the lower bound of the
confidence interval was 1.0 (indicating borderline statistical significance), Dr. Jewell was
comfortable ruling out the possibility that the result was obtained by chance, notwithstanding the
fact that the odds ratio was no longer statistically significant. 28
The Court is persuaded that Dr. Jewell did review the then-available evidence and rely
upon replication of statistically significant (and borderline significant) results in forming the
opinion expressed in his expert report and at the Daubert hearing. However, as discussed above,
when he was confronted with new and contrary information at the Daubert hearing, such as the
Furu (2015) results or the extent of data overlap, and given the opportunity to address it, he did
not reconcile this information with his opinions.
Also of concern to the Court is that the more recent studies, including but not limited to
Furu (2015), which have been well-powered and designed to address issues of bias and
confounding, have not confirmed the positive associations between Zoloft and cardiac birth
defects reported in the studies Dr. Jewell relies upon as evidence of replicated positive findings.
Beginning with Jimenez-Solem (2012), researchers have designed their studies to address issues
of confounding and bias. With the exception of Bérard (2015) (discussed above), authors of the
recent studies (e.g. Ban (2014); Huybrechts (2014); Furu (2015)) have uniformly failed to
replicate the associations noted in early studies, and the study authors have concluded that the
27

Doc. No. 1481, Tr. at 156-57. See also, Doc. No. 1483, Tr. at 34-35.

28

Doc. No. 1481, Tr. at 160.

16

reported association between Zoloft and cardiac birth defects may have been the result of chance,
confounding by indication, or other confounders. The Court must, then, examine whether Dr.
Jewell used reliable scientific methods in concluding that the non-confirming study results, and
especially the recent studies, were not incompatible with his causation opinion.
2.

Use of Non-Statistically Significant Study Results

Pfizer argues that Dr. Jewell’s opinion is not based upon reliable methodology because,
like Dr. Bérard, whose expert opinion was previously excluded by the Court, he downplays the
importance of statistical significance and relies upon “trends” in odds ratios rather than
replication of statistically significant results.
The Court agrees that Dr. Jewell’s approach to the Zoloft data de-emphasizes the
traditional importance of statistical significance. Dr. Jewell notes that a non-significant result
“does not tell us that the exposure has no effect—only that ‘no effect’ remains one of the
plausible explanations for the data (but not necessarily the most plausible).” 29 Like Dr. Bérard,
he cites to Rothman’s Modern Epidemiology textbook for the principle that it is “generally
accepted to examine the effect estimates (i.e., Odds Ratio) without exclusion of non-significant
results.” 30 Like Dr. Bérard, he points to no other evidence indicating that the fields of
epidemiology and teratology have abandoned, or even reduced the importance of, the principle
of statistical significance. The New England Journal of Medicine’s treatment of the correction to
the Louik (2007) study provides evidence to the contrary.
In some circumstances, experts may use congruent but non-significant data to bolster
inferences drawn from replicated, statistically significant data. However, in this case, as
discussed above, three of the studies Dr. Jewell relies upon to show replication use overlapping
29

Report at 27.

30

Report at 29.

17

data from the Danish population, and the early studies suggesting a doubling or tripling of risk
have not been replicated by later, well-powered studies which attempt to control for various
confounding factors and biases. Thus, the Court must examine how Dr. Jewell addresses nonsignificant data more closely.
Dr. Jewell’s causation opinion relies, in part, upon trends he observes in odds ratios
(although Dr. Jewell has assiduously avoided using the term “trend”). Dr. Jewell explained that
he believes consistent odds ratios across studies are telling, even if they do not reach statistical
significance. He explains that odds ratios may be non-significant because a study is
underpowered, or the outcome is biased towards the null hypothesis by misclassification of
exposure. 31 In one section of his report, he points out the unlikelihood of finding a consistent
increase in risk over different data sets purely by chance. 32 His report provides a hypothetical
example: where two independent studies 33 report a positive association with a non-significant pvalue of .1, he multiplies the p-values together and concludes that “observing a one in ten (0.1)
chance event twice occurs with a probability 0.01.” 34 This method of multiplying p-values from
independent studies is not a method used in the peer-reviewed, published meta-analyses the
Court has reviewed, and the Court has been presented with no evidence that it is accepted as a
scientifically reliable methodology. Moreover, although Dr. Jewell’s report describes this
mathematical approach to determine whether associations are true or the result of chance, Dr.
31

Dr. Jewell points out that some women falsely report taking Zoloft when they did not; others deny taking
it when, in fact, they did. Either type of misclassification of exposure can bias the observed association towards the
null. Report at 48-49. Both commonly utilized methods for determining exposure (pharmacy dispensing records and
maternal self-report) carry a risk of misclassification of exposure, but because researchers cannot assign women to
treatment and placebo groups when studying potential teratogens, they must utilize these imperfect methods for
assessing exposure.
32

Report at 32-33.

33

Several of the studies which report a doubling or tripling of risk rely upon overlapping patient data, and
such studies cannot be considered independent.
34

Report at 32.

18

Jewell does not then apply this mathematical approach to the p-values that can be calculated
from results reported in independent studies of Zoloft and cardiac birth defects. 35 Instead, he
generalizes, concluding that “the generally consistent findings of cardiovascular birth defects
associated with maternal Zoloft exposure during the relevant period of pregnancy indicates that
these are true, rather than false, positive findings.” 36 Even if the multiplication of p-values were a
reliable method for ruling out the role of chance, the Court finds Dr. Jewell’s failure to apply the
methodology he outlined to the studies he reviewed problematic.
The Court also notes with concern that, although Dr. Jewell relies heavily upon trends
and “general consistency” in the data for his opinion, he selectively emphasizes observed
consistency between studies of Zoloft and cardiac birth defects only when the consistent studies
support his opinion. Dr. Jewell does not squarely address why, if there is a true association
between Zoloft and cardiac birth defects, relatively few studies report odds ratios consistent with
a doubling or tripling of risk (whether significant or non-significant), and most report data
consistent with the authors’ conclusions that there is no true association. While he critiques
individual studies, he does not explain why he considers generally consistent associations so
telling only when they support his opinion. In addition to the small number of studies which
reported odds ratios consistent with a doubling or tripling of risk of cardiac birth defects, many
studies have reported odds ratios consistent with no increase in risk. Yet, Dr. Jewell does not
address the cumulative evidence indicative of no association. For example, two recent studies
from large, independent data sets, Huybrechts (2014) and Furu (2015), reported consistent odds
ratios—but these odds ratios were close to one, suggesting no increased risk. Huybrechts (2014)
35

Although the studies themselves do not report p-values, but only report the odds ratios and confidence
intervals, it appears from his report that Dr. Jewell is able to calculate the p-values from the published information.
See Report at 32.
36

Report at 33.

19

reported an odds ratio of 1.09 for all cardiac defects and 1.04 for ventricular septal defects, and
Furu (2015) reported an odds ratio of 1.13 for all cardiac defects and 1.05 for septal defects. In
contrast to his treatment of cumulative evidence in support of his opinion, when he approached
such cumulative evidence which is not supportive of his opinion, Dr. Jewell did not address the
likelihood that two or more studies would report odds ratios approaching one if there were a true
association between Zoloft and birth defects (i.e. the likelihood that both studies would fail to
detect a true increase in risk). Dr. Jewell’s selective emphasis on trends and general consistency
only when such concepts support his opinion is one example of “situational science” which
renders his opinion unreliable. Another will be discussed in the section to follow.
3.

“Situational Science” and Heterogeneity

According to Dr. Jewell, “[h]eterogeneity is the measure of the variation among the effect
sizes reported in [various] studies [and] . . . where heterogeneity is significant, the source of
variation should be investigated and discussed.” 37 When heterogeneity is statistically significant,
that means the variability between studies is not mere “noise” in the data; it is greater than one
would expect by chance variations between studies, and must be accounted for by the
researchers.
Dr. Jewell explained to the Court that, when researchers are conducting a meta-analysis
(a well-established statistical technique used to combine data from multiple studies, often used
when individual studies suggest the possibility of an association between two variables, but are
unable to rule out the role of chance due to a lack of statistical power), the authors must measure
the degree of variability (heterogeneity) between the parallel results of the studies included in the
meta-analysis. Meta-analyses are reliable, Dr. Jewell said, when the results from the individual

37

Report at 57.

20

studies being combined are similar (homogeneous), but may be unreliable when the results of the
individual studies are variable (heterogeneous), 38 if the variation between studies is not properly
addressed. Heterogeneity between studies is not assessed subjectively, but is assessed using an
objective statistical formula, which produces an estimate of heterogeneity and also a “p-value,”
which indicates whether the estimate of heterogeneity is statistically significant.
Pfizer produced evidence that Dr. Jewell relied heavily on two meta-analyses, Myles
(2013) and McDonagh (2014) (both published, peer-reviewed, meta-analyses of the effects of
maternal SSRI use during pregnancy) as a plaintiffs’ expert in the Prozac birth defects
litigation, 39 but he does not rely upon those same studies as a plaintiffs’ expert in the Zoloft
litigation. Pfizer suggests that his variable reliance on the same studies in the two cases is results
driven: Myles (2013) and McDonagh (2014) reported statistically significant associations
between Prozac and the outcomes of interest in the Prozac MDL, but did not report statistically
significant associations between Zoloft and the outcomes of interest in this MDL.
Dr. Jewell testified that his variable reliance on these two studies was driven by
methodology, not results, and his explanation relied heavily upon the principle of
heterogeneity. 40 He explained that, in the Myles (2013) study, heterogeneity was statistically
significant for Zoloft and cardiac birth defects, but not for Prozac. Because heterogeneity was not
an issue in the Prozac data, Dr. Jewell testified that he felt more comfortable relying upon those
results. Dr. Jewell testified that “you can’t use a single statistical tool in all situations blindly as
if it will give you interpretable results, ignoring what the data itself is telling you about whether

38

See Report at 57-58.

39

Like Zoloft, Prozac is an SSRI used for treatment of depression.

40

See Report at 57; Doc. No. 1481, Tr. at 138-140.

21

the tool is valid or not.” 41 While the Court finds that Dr. Jewell has set forth a scientific
rationale for his variable reliance on relevant meta-analyses in the Prozac MDL and the Zoloft
MDL, the Court is concerned that Dr. Jewell selectively relies upon the principle of
heterogeneity in a results-driven manner.
That is, Dr. Jewell’s discussion of heterogeneity with regard to the meta-analyses raises
another concern about “situational science,” and Dr. Jewell’s failure to consistently apply
statistical methods he identifies as important to the data at hand. While the heterogeneity of the
study results may well be an acceptable explanation for Dr. Jewell’s skepticism towards the
Zoloft findings contained in the Myles (2013) and McDonough (2014) meta-analyses, what
concerns the Court is that Dr. Jewell makes much of what he characterizes as the “generally
consistent” results across the 11 core studies, but fails to statistically calculate the heterogeneity
of those 11 studies, instead focusing on a subjective general consistency among the odds ratios in
published Zoloft studies. As Dr. Jewell testified that the heterogeneity between studies can be
determined statistically, and as he relies upon those statistical measures of heterogeneity in
critiquing the quality of key meta-analyses, the Court finds that Dr. Jewell’s reliance on his
subjective observation that the reported odds ratios from various studies are “generally
consistent,” in another part of his report and in other portions of his testimony, is not based upon
any reliable statistical or scientific method. As a statistician, the Court would expect Dr. Jewell
to rely upon statistical methods to determine the degree of consistency (or variability), not rely
upon trends and generalities, but he again places importance upon statistical principles when they
support his opinion, and ignores them when they do not. This cannot be considered a valid
methodology.

41

Doc. No. 1481, Tr. at 134.

22

C.

Use of Company Documents

Pfizer also argues that Dr. Jewell inappropriately relies upon statements set forth in Pfizer
company documents which are themselves literature reviews, not studies reporting original data,
and which are not typical of documents statisticians would generally rely upon in a causation
analysis. For example, Pfizer argues, Dr. Jewell relies upon a preliminary draft of a Periodic
Safety Update Report (PSUR) concerning growth retardation in children and adolescents, and
misrepresents both the purpose of the document and the meaningfulness of the statements he
quotes. Pfizer notes that the PSUR does not report on any safety studies conducted by Pfizer
regarding use during pregnancy, and as such, it should not be of any interest to a statistician.
In his testimony, Dr. Jewell noted that what interested him about Pfizer’s report was the
methodology used by Pfizer’s employees, not the opinions stated. 42 “[T]hey were doing the same
kind of thing, albeit in a smaller set of studies as was pointed out this morning by [Pfizer’s
counsel] Mr. Cheffo, but the methodology was still the same. You take the studies, you look at
them and you see if there’s a consistent positive association.” 43
To the extent that Dr. Jewell does rely upon the PSUR as supportive of his conclusions,
rather than his methods, the Court notes that the portion of the PSUR Dr. Jewell relies upon is in
the form of a brief review of only three published studies, and all three reported statistically
significant associations between Zoloft and cardiac birth defects (one of which, Louik (2007),
was later corrected to report a non-significant association). It was appropriate for Dr. Jewell
himself to review and assess any studies Pfizer cited in the PSUR, and he did this in his expert
report. However, the Court has not been presented with any evidence from which it can find that
the PSUR is the type of document statisticians such as Dr. Jewell generally rely upon to support
42

Doc. No. 1482, Tr. at 53.

43

Id.

23

their opinions. The cited studies themselves are a better source of information regarding the
methods used and the results of studies of the association of interest, and it is the methods, data,
and results that a statistical expert such as Dr. Jewell is called upon to interpret.
The same is true regarding the email summarizing a preliminary literature review (of five
studies) conducted by a Pfizer employee in conjunction with a request from the FDA to update
the Zoloft label.
The PSC has not demonstrated that Dr. Jewell is the proper witness through which to
introduce these documents, as such partial literature reviews are not the kinds of information
generally relied upon by statisticians, whether to support their conclusions or their methods. In
addition, to the extent that the documents report Pfizer’s preliminary concerns about product
safety, warranting further investigation, and not final conclusions drawn by Pfizer (as Pfizer
argued at the hearing), Dr. Jewell’s use of them would potentially be misleading to a jury.
D.

Re-Analyses on the Issue of Confounding by Indication

Because the research results regarding Zoloft and cardiac birth defects are equivocal, an
expert could assist the trier of fact by opining as to whether the results showing a positive
association are indicating an association that is not truly present (a false positive), or the results
which show little or no association are failing to detect an effect that is present (a false negative).
That opinion must, of course, be based upon reliable methodology.
With regard to Zoloft and cardiac birth defects, some researchers have hypothesized that
the association detected in early studies resulted from confounding, and is not a true association.
Because confounding by indication is one important source of potential bias, researchers have
designed studies to test the hypothesis that confounding by indication accounts for any increased
risk. For example, they may look at whether the risks are similar in women taking Zoloft and

24

non-SSRI antidepressants, or by examining whether the risks are similar in women who take
Zoloft during the first trimester and those who are prescribed an SSRI but “pause” their use of it
during the first trimester, as Jimenez-Solem (2012) did. 44 Dr. Jewell opines that the associations
reported in early studies reflect true associations between Zoloft and cardiac birth defects, and
hypothesizes that confounding by indication cannot account for the statistically significant
associations reported in the early studies.
After he was retained as a Plaintiffs’ expert in this litigation, and for the purpose of
supporting his testimony in this litigation, Dr. Jewell reanalyzed certain data from published,
peer-reviewed studies and concluded that his own analyses support his hypothesis that
confounding by indication is an insignificant factor, accounting for no more than a 10% increase
in risk over the background risk for cardiac birth defects. 45 This Court must determine whether
Dr. Jewell used reliable principles and methods in addressing the issue of confounding by
indication in his re-analyses.
First, Dr. Jewell explains that if reported positive associations between Zoloft and cardiac
birth defects were the result of confounding by indication, researchers would consistently detect
a similar increased risk in both the “exposed” group and the “paused” group. Jimenez-Solem
(2012) reported an odds ratio of 2.73 for the “exposed” group, and an odds ratio of 1.85 for the
“paused” group—both statistically significant increases in risk compared to those who were
unexposed—and concluded that a confounding factor present in both groups, rather than Zoloft,
was accounting for most of the increase in risk reported in her study. Dr. Jewell, however, was
44

See Jimenez-Solem (2012) (finding the SSRI paused group and the Zoloft exposed group were at
similarly heightened (approximately tripled) risk of cardiac malformations). Cf., Kornum (2010) (finding neither
the Zoloft exposed nor the paused group were at heightened risk of congenital malformations overall (odds ratios 1.4
and 1.1, respectively) and not reporting regarding whether the paused group was at heightened risk of cardiac birth
defects).
45

Dr. Jewell cites Ban (2014) in support of his assertion that confounding by indication accounts for no
more than a 10% increase in risk.

25

not persuaded by the analysis of confounding set forth in this peer reviewed, published study. He
ran a supplemental analysis, directly comparing the increase in risk in the “exposed” group to the
increase in risk in the “paused” group, 46 and found that although those in both groups were at
significantly increased risk compared to the general population, those in the Zoloft-exposed
group were at greater risk compared to those in the “paused” group. Thus, although the study
authors concluded from their results that confounding was an important factor in the increased
risk detected in the exposed group, Dr. Jewell rejected that conclusion, and based upon his own
post hoc analysis, explained above, he concluded that the conclusions of the study authors were
incorrect.
Next, Dr. Jewell reanalyzed data from the Huybrechts (2014) study. Huybrechts (2014),
which was recently published in the New England Journal of Medicine, included almost one
million women, 14,000 of whom the authors categorized as “exposed” to Zoloft during the first
trimester. The exposed group included both: 1) women who had filled a prescription for Zoloft
after their last menstrual period but prior to a positive pregnancy test and who had sufficient pills
to last through at least part of the first trimester, 47 and 2) women who had filled or refilled a
prescription at least once during their first trimester. Dr. Kimmel reported that the study had
sufficient power to identify a doubling of risk of cardiac defects among the children of Zoloft
exposed mother, if such an association existed. 48 The study, however, found no such association,
reporting odds ratios near one (e.g., 1.09 for cardiac malformations overall) and narrow
confidence intervals, indicating that the odds ratios were fairly precise estimates of relative risk.

46

The study authors compared both groups to their sample of unexposed women, but did not directly
compare the exposed group to the paused group.
47

The parties agree that the critical period for cardiac development is early in the first trimester.

48

Kimmel Report at 5.

26

Dr. Jewell opined that the subset of women who filled a Zoloft prescription after their last
menstrual period and had sufficient pills to overlap with at least part of the first trimester, but
who did not refill it during the first trimester, are more properly considered “paused” users. That
is, he assumes that those women stopped taking Zoloft by the start of the first trimester, whereas
the study authors assumed that they did not. Dr. Jewell does not indicate what information he
relied upon in rejecting the assumption of the study authors and drawing his own assumption
about women’s medication use. According to the study’s methods section, the study used only
pharmacy dispensing records (including the date Zoloft was dispensed and the number of pills
supplied) to determine first trimester Zoloft exposure; the authors did not conduct interviews,
collect patient questionnaires, review doctors’ records, or compile other sources which could be
used to test Dr. Jewell’s assumption that these women did not take the dispensed medication
during the first trimester. Dr. Jewell is not an obstetrician, psychiatrist, or medical doctor, and
therefore presumably has no first-hand experience regarding the likelihood that women will
continue to take prescribed antidepressant medication during pregnancy. And he does not cite to
any studies which addressed that issue. The Court finds that Dr. Jewell provides no scientific
rationale for discarding the assumption of the study authors, who have greater knowledge of both
the subject matter and the data itself, that women who had a prescription with enough pills to
extend into the first trimester were exposed, and substituting his own assumption that those
women were unexposed. Thus, it appears that Dr. Jewell formed his assumption that women in
this group “paused” their Zoloft use during the first trimester without any scientific foundation.
This methodological flaw may have been of less importance if Dr. Jewell had simply
opted, based upon his assumption that many of the women who had a supply overlap but did not
refill their Zoloft prescription during the first trimester were unexposed, to reanalyze the risk

27

using the subset of Huybrechts (2014)’s exposed subjects who had filled or refilled one or more
Zoloft prescription during the first trimester. He did not do this, perhaps because Huybrechts
(2014) recognized the potential for misclassification of exposure, and ran secondary analyses
using only women who had filled or refilled a prescription once or twice during the first
trimester. 49 The results of these secondary analyses were substantially the same, although the
confidence intervals were wider due to the decreased number of subjects (decreasing statistical
power). 50 The authors concluded from these sub-analyses that misclassification of exposure in
the no-refill group was unlikely to bias the overall study results towards the null. These subanalyses directly address Dr. Jewell’s criticism of the study results as potentially biased towards
the null hypothesis by misclassification of exposure.
Dr. Jewell conducted an additional statistical analysis of the Huybrechts (2014) data, to
examine his hypothesis that confounding by indication did not explain the heightened risk of
cardiac birth defects in earlier studies. In this analysis, Dr. Jewell compared the outcomes for
women whom he considered “paused” to the women he considered “exposed.” The results
indicated that women in his “exposed” group were at an increased risk of giving birth to a baby
with a congenital heart defect, compared to those in his “paused” group. 51 Because both the
women in his “paused” group and those who refilled one or more Zoloft prescriptions in the first
trimester shared indications warranting a Zoloft prescription, Dr. Jewell interpreted the
difference in risk between the two groups as supportive of his hypothesis that confounding by

49

Huybrechts (2014) at 2405.

50

Id.

51
Report at 41. In conducting and interpreting this reanalysis, Dr. Jewell makes much of the difference
between his two groups, and downplays the fact that neither group is at a heightened risk of giving birth to a child
with birth defects when compared to unexposed women.

28

indication does not fully explain the positive associations between Zoloft and cardiac birth
defects noted in some studies. 52
It is appropriate for a statistician to design a study and statistically analyze the data
collected when testing a hypothesis. However, results-oriented, post-hoc re-analyses of existing
epidemiological studies are disfavored by scientists and often deemed unreliable by courts,
unless the expert can validate the need for reanalysis in some way. 53 Although Dr. Jewell reports
that he ran his re-analysis of the Huybrechts (2014) data in order to address the unexamined issue
of confounding by indication, study author Krista Huybrechts, Ph. D. noted, in correspondence
with Dr. Jewell, that it makes no scientific sense to look, post hoc, at the issue of confounding by
indication when no association between Zoloft use and cardiac birth defects was found in the
primary analyses. 54 That is, in conducting his additional analysis, Dr. Jewell seemed to disregard
the authors’ reported finding that women who were exposed to Zoloft were no more likely than
unexposed women to have children with cardiac birth defects. Dr. Huybrechts also challenged
Dr. Jewell’s definition of “paused” and indicated that she felt his approach to potential
confounding variables was flawed. 55 Dr. Jewell did not explain to the Court why his reanalysis to
assess the role of confounding was a proper one under the circumstances. These concerns,
combined with the problem of Dr. Jewell’s re-classification of subjects’ exposure to Zoloft
without an articulated scientific foundation, are methodological flaws which undermine the
52

Because Daubert requires the Court to focus on methodology, the Court cannot consider whether Dr.
Jewell has reached a correct conclusion regarding the role of confounding by indication, but may only examine his
methods.
53

In re: Lipitor (Atorvastatin Calcium) Marketing, Sales Practices and Products Liability Litigation, 14md-2502, Memorandum Opinion and Order dated November 20, 2015 at 32 (collecting cases).
54

Email dated 7/9/15 from K. Huybrechts to N. Jewell (“Doing such an analysis would be useful if there
was a concern about residual confounding by depression and its associated factors in the main analysis. Given that
the overall analysis showed a null association and residual confounding is not a concern, what would be the value of
such analysis?”). See also email dated 7/12/15 from K. Huybrechts to N. Jewell.
55

Email dated 7/9/15 from K. Huybrechts to N. Jewell. See also email dated 7/12/15 from K. Huybrechts to

N. Jewell.

29

reliability of his analysis. For these reasons, the Court finds that Dr. Jewell’s post-hoc reanalysis
of the Huybrechts (2014) data is not reliable and should not be presented to the jury.
The Court also has concerns about the methodology used in a two-study meta-analysis
Dr. Jewell performed, which combined data from Jimenez-Solem’s paused SSRI users (defined
in her study as those who stopped taking an SSRI at least 3 months before becoming pregnant,
and resumed taking it within a year after the baby was born), 56 with data from women in the
Huybrechts (2014) study that Dr. Jewell categorized as “paused” using different criteria (women
who filled a prescription for Zoloft after their last menstrual period before conception, with a pill
supply overlapping with the first trimester, but who did not refill the prescription in the first
trimester).
The Court identifies several problems with Dr. Jewell’s meta-analysis. First, Dr. Jewell
failed to present a scientific rationale for combining these two data sets in a meta-analysis (for
example, he has not demonstrated that the studies, standing alone, lacked statistical power to
study the questions of interest to him). Second, he fails to explain why he limited himself to
those two studies, and did not also include studies such as Kornum (2010), which also included a
“paused” group. Third, the definitions of “paused” used by Jimenez-Solem and Dr. Jewell are
56

Although Dr. Jewell relied upon “paused” group data from Jimenez-Solem (2014), he expressed concerns
about the definition of “paused” used in the study. First, Dr. Jewell criticized Jimenez-Solem (2012) for combining
paused users of all SSRIs in their “paused” group. However, he offers no scientific rationale for analyzing paused
users of each SSRI separately. The purpose of the “paused” versus “exposed” comparison is to examine issues of
confounding by indication. In Jimenez-Solem (2012), “paused” subjects have no prenatal exposure to an SSRI, but
share indicators for prescription of an SSRI. Neither side has presented evidence that the indicators for the various
SSRIs are different. Therefore, the Court perceives no flaw with Jimenez-Solem’s combining paused users of any
SSRI, and doing so provides the advantage of greater statistical power.
Dr. Jewell also criticizes Jimenez-Solem’s definition of “paused,” speculating that if one had a child with a
heart defect, one might be more likely to resume taking an antidepressant; therefore, he said that the very definition
of “paused” might select into the “paused” group a disproportionate number of non-exposed women who had
adverse outcomes. Dr. Jewell is not a psychiatrist or medical doctor, and therefore presumably has no first-hand
experience regarding the likelihood that women will resume taking antidepressant medication after pregnancy, but
the Court acknowledges the potential for bias inherent in the definition chosen by the study authors. However, Dr.
Jewell did not statistically estimate for the Court how large an impact such a bias could have on the results, given
the rarity of the cardiac conditions at issue.

30

completely different, such that women categorized as “paused” by Jimenez-Solem would be
characterized as unexposed (not as “paused”) in the Huybrechts sample, using Dr. Jewell’s
definition, and women Dr. Jewell categorizes as “paused” in the Huybrechts sample would be
categorized as “exposed” by Jimenez-Solem (as they were by Huybrechts in the published
study). Dr. Jewell has not established that it is scientifically acceptable to perform a metaanalysis combining data on “exposed” and “paused” women from two different studies when the
two categories are so differently defined in the underlying studies being combined. Thus, the
Court concludes that no reliable inferences or conclusions can be drawn from the meta-analysis
conducted by Dr. Jewell for purposes of this litigation. 57
III.

CONCLUSION
Dr. Jewell opines that Zoloft may cause cardiac birth defects. His opinion is based upon

his review and analysis of the relevant literature, and his own reanalysis of certain data from
published studies. Plaintiffs argue that Dr. Jewell employed the well-accepted methods of Dr.
Jewell’s profession as a statistician in arriving at his opinion. Pfizer has argued that while Dr.
Jewell outlines a valid method for assessing the literature regarding Zoloft and cardiac birth
defects, he has not faithfully applied that methodology.
“The reliability analysis [required by Daubert] applies to all aspects of an expert’s
testimony: the methodology, the facts underlying the expert’s opinion, [and] the link between the
facts and the conclusion.” 58 As an expert witness in statistics, faced with equivocal evidence in

57

In addition to the methodological problems outlined, Daubert instructs the Court to consider that both the
reanalysis of Huybrechts (2014) data, and the meta-analysis of the Jimenez-Solem (2012) and the Huybrechts (2014)
data, were conducted by Dr. Jewell solely for the purpose of this litigation, without the participation of scientists
with expertise in the fields of embryology and teratology, and have not been subjected to peer review or publication.
These issues also weigh against admissibility of the independent analyses.
58

ZF Meritor, LLC v. Eaton Corp., 696 F.3d 254, 291 (3d Cir. 2012) (internal quotation omitted).

31

this case, Dr. Jewell would be called upon to: 1) explain why he believes that the positive
associations between maternal Zoloft use and cardiac birth defects, reported in some papers, are
true associations, and not the result of a study flaw, confounding, bias, or other factor; and 2)
reconcile those studies which reported no increased risk of cardiac birth defects with his opinion.
Because of the methodological weaknesses identified herein, the Court concludes that, in
addressing both issues, Dr. Jewell, inter alia, has failed to consistently apply the scientific
methods he articulates, has deviated from or downplayed certain well-established principles of
his field, and has inconsistently applied methods and standards to the data so as to support his a
priori opinion. It is improper for an expert to take a results-driven approach to a question,
molding his methodology and selectively relying upon data so as to confirm his preconceived
opinion. Because the Court finds that Dr. Jewell’s opinion is based upon his failure to faithfully
apply reliable scientific and statistical methods and because his testimony is likely to confuse or
mislead the jury, the Court will exclude his testimony at trial under Federal Rules of Evidence
403 and 702, and the principles outlined in Daubert.

32

Appendix A

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i'

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i ~,', , , ';;i;~;:l::::~'/i,ii , {,iii:'"{
'(

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, , ,,',<',,)111,

,,

i',~,,:,~h

1.

Adams v. Pfizer, Inc.

14-2247

2.

Aleshire et al v. Pfizer Inc. et al.

13-2734

3.

Allen v. Pfizer, Inc., et al.

13-7375

4.

Amadio v. Pfizer, Inc.

11-3973

5.

Amons v. Pfizer Inc., et al.

13-2737

6.

Anderson v. Pfizer Inc.

12-2126

7.

Asboe v. WKH, et al.

12-2686

8.

Ascherman v. Pfizer, Inc.

15-530

9.

Asphall v. Pfizer, Inc.

12-4091

10.

Ball-Andrews v. Pfizer, Inc., et al.

12-5118

11.

Beverly v. Pfizer, Inc., et al.

12-5118

12.

Leffv. Pfizer, Inc., et al.

12-5118

13.

Pallaria v. Pfizer, Inc., et al.

12-5118

14.

Putman v. Pfizer, Inc., et al.

12-5118

15.

Retzer v. Pfizer, Inc., et al.

12-5118

16.

Root v. Pfizer, Inc., et al.

12-5118

17.

Sturdivant v. Pfizer, Inc., et al.

12-5118

18.

Farugia v. Pfizer, Inc., et al.

13-3234

19.

Farugia v. Pfizer, Inc., et al.

13-3234

20.

Honn v. Pfizer, Inc., et al.

13-3234

21.

Pardo v. Pfizer, Inc., et al.

13-3234

22.

Schedlbauer v. Pfizer, Inc., et al.

13-3234

23.

Thompson v. Pfizer, Inc., et al.

13-3234

24.

Barnes v. WKH, et al

12-213

25.

Barnette v. Pfizer, Inc.

14-147

26.

Bassett et al v. Pfizer, Inc. et al

13-6296

27.

Bell v. Pfizer, Inc.

12-6252

3

I

28.

Bell v. WKH, et al.

12-2754

29.

Bell v. Pfizer, Inc. et al

14-716

30.

Bellemore et al v. Pfizer, Inc. et al

14-1427

31.

Bell-Rumora v. Pfizer Inc.

12-2444

32.

Bennett et al v. Pfizer Inc. et al

13-7129

33.

Berg v. Pfizer Inc., et al.

13-724

34.

Bertelson v. Pfizer Inc.

13-7511

35.

Bishop v. Pfizer Inc. et al.

13-5945

36.

Bolton v. Pfizer Inc.

13-4806

37.

Booker v. Pfizer Inc., et al.

13-2740

38.

Boyd et al v. Pfizer Inc. et al

14-850

39.

Branscome et al v. Pfizer Inc. et al.

14-620

40.

Brodt v. Pfizer, Inc.

13-1883

41.

Brodt v. Pfizer, Inc.

13-1882

42.

Brooks v. Pfizer, Inc.

13-872

43.

Brown v. Pfizer, Inc. et al

14-1329

44.

Brownback v. Pfizer, Inc., et al.

13-3339

45.

Bryan v. Pfizer Inc. et al.

13-5587

46.

Bufford v. Greenstone LLC, et al.

12-2336

47.

Buley v. Pfizer Inc.

13-63

48.

Buneta v. Pfizer, Inc.

14-279

49.

Burnett v. Pfizer, Inc et al

14-2519

50.

Burton v. Pfizer, Inc. et al

14-6770

51.

Butterworth v. Pfizer Inc. et al.

13-5690

52.

Byington v. Pfizer, Inc.

12-5435

53.

Cable v. Pfizer Inc., et al.

13-4025

54.

Caldwell v. Pfizer

15-1678

55.

Caldwell v. Pfizer, Inc., et al

12-2557

56.

Carr v. Pfizer Inc. et al

15-2655

4

57.

Case v. Pfizer, Inc. et al

14-6552

58.

Casi v. WKH, et al.

12-247

59.

Castello v. Pfizer Inc., et al.

13-2741

60.

Castillo v. WKH, et al

12-243

61.

Chapman et al v. Pfizer, Inc. et al

14-743

62.

Chapman v. Pfizer Inc.

13-4807

63.

Chatman v. Pfizer, Inc.

13-225

64.

Chew v. Pfizer Inc., et al.

13-2723

65.

Cho v. Pfizer Inc.

12-6664

66.

Christianson v. WKH, et al.

12-216

67.

Chumley et al v. Pfizer, Inc. et al

13-6240

68.

Ciccone v. Pfizer, Inc.

12-5952

69.

Cipher v. Pfizer Inc. et al

14-5818

70.

Clarizio v. Pfizer, Inc., et al

13-4106

71.

Clark v. Pfizer, Inc., et al.

13-1709

72.

Cloin v. Pfizer, Inc.

12-2337

73.

Cochran v. Pfizer, Inc.

13-900

74.

Cochran v. Pfizer, Inc.

13-1861

75.

Coffey et al v. Pfizer, Inc. et al

13-6353

76.

Cokeley v. Pfizer, Inc. et al.

13-1166

77.

Collins v. Pfizer, Inc.

12-3113

78.

Combs v. Pfizer, Inc.

14-179

79.

Cook v. Pfizer, Inc.

13-1794

80.

Coon v. Pfizer Inc., et al.

12-4711

81.

Coronado v. Pfizer Inc., et al.

14-2490

82.

Crabtree v. Pfizer, Inc.

13-365

83.

Craghead v. Pfizer, Inc.

13-594

84.

Crawford v. Pfizer, Inc et al

14-5591

85.

Crozier et al v. Pfizer, Inc. et al

14-906

5

86.

D'Ambrosio v. Pfizer, Inc., et al.

13-341

87.

Davis et al v. Pfizer, Inc. et al

13-6132

88.

Demartini et al v. Pfizer, Inc.

14-2204

89.

Demedio v. Pfizer, Inc., et al.

13-482

90.

Dennison v. Pfizer, Inc.

13-593

91.

Derome v. Pfizer Inc.

14-5590

92.

DeShields v. Pfizer Inc.

13-7515

93.

DeSimone v. Pfizer Inc., et al

12-2435

94.

Diaz v. Pfizer, Inc.

13-2620

95.

Becker v. Pfizer, Inc., et al.

12-5119

96.

Currier v. Pfizer, Inc., et al.

12-5119

97.

Dorste v. Pfizer, Inc., et al.

12-5119

98.

George v. Pfizer, Inc., et al.

12-5119

99.

Hardy v. Pfizer, Inc., et al.

12-5119

100.

Hill v. Pfizer, Inc., et al.

12-5119

101.

Martynuik v. Pfizer, Inc., et al.

12-5119

102.

Paris v. Pfizer, Inc., et al.

12-5119

103.

Doyle v. Pfizer, Inc

12-5371

104.

Dragon v. Pfizer, Inc.

14-3114

105.

DuBois v. Pfizer, Inc.

13-1224

106.

Greene v. Pfizer, Inc.

13-6162

107.

Hudson v. Pfizer, Inc.

13-6162

108.

Leffv. Pfizer, Inc.

13-6162

109.

Leslie v. Pfizer, Inc.

13-6162

110.

Madewell v. Pfizer, Inc.

13-6162

111.

Parkinson v. Pfizer, Inc.

13-6162

112.

Patterson v. Pfizer, Inc.

13-6162

113.

Pittman v. Pfizer, Inc.

13-6162

114.

Dundas v. Pfizer, Inc.

13-3818

6

115.

Dyer et al v. Pfizer Inc. et al

14-2342

116.

Easley v. Pfizer Inc.

14-4482

117.

Ebright v. Pfizer, Inc.

12-5678

118.

Emlen v. WKH, et al

12-226

119.

Ero! et al v. Pfizer, Inc. et al

13-6915

120.

Estock v. Pfizer Inc.

12-6520

121.

Falk v. WKH, et al.

12-2750

122.

Favata v. Pfizer Inc., et al.

12-5622

123.

Ferris v. Pfizer Inc.

13-3963

124.

Fitts v. Pfizer, Inc.

12-3094

125.

Flores v. Pfizer Inc.

13-2733

126.

Floyd v. Pfizer Inc., et al

13-54

127.

Franklin v. Pfizer Inc.

13-2274

128.

Gallegos v. Pfizer,Inc.

13-1019

129.

Gardner v. Pfizer, Inc.

12-4090

130.

Gearhart v. Pfizer Inc., et al

13-158

131.

Gibbs v. Pfizer Inc et al

14-2521

132.

Gilcrest v. Pfizer, Inc.

13-1282

133.

Gillette v. Pfizer, Inc., et al.

12-6772

134.

Gilmore v. Pfizer Inc.

13-2270

135.

Godwin v. Pfizer Inc. et al

13-4602

136.

Goulet v. Pfizer Inc., et al

12-2441

137.

Graves v. pfizer, Inc.

13-1072

138.

Gregory v. Pfizer Inc., et al

12-2436

139.

Griggs v. Pfizer Inc.

13-2276

140.

Grotelueschen v. Pfizer Inc., et al.

14-2284

141.

Gully v. WKH, et al

12-255

142.

Haddad v. Pfizer Inc.

14-5331

143.

Hagar v. Pfizer, Inc.

14-1949

7

144.

Hammons v. Pfizer Inc., et al

12-6770

145.

Harbin v. Pfizer, Inc., et al.

14-4204

146.

Beilke v. Pfizer, Inc., et al.

12-5122

147.

Cox v. Pfizer, Inc., et al.

12-5122

148.

Grube v. Pfizer, Inc., et al.

12-5122

149.

Hardin v. Pfizer, Inc., et al.

12-5122

150.

Havens v. Pfizer, Inc., et al.

12-5122

151.

Markle v. Pfizer, Inc., et al.

12-5122

152.

Hardy v. Pfizer Inc., et al.

13-4234

153.

Haring v. Pfizer, Inc., et al

12-3485

154.

Harrelson et al v. Pfizer Inc. et al

15-227

155.

Harris et al. v. Pfizer Inc. et al.

14-685

156.

Harrison v. Pfizer, Inc.

13-2750

157.

Harrison v. Pfizer, Inc.

12-6448

158.

Hart v. Pfizer, Inc.

13-1184

159.

Hartley v. Pfizer, Inc., et al.

12-2796

160.

Hauguel et al v. Pfizer, Inc. et al

14-849

161.

Hawkins et al v. Pfizer, Inc. et al

14-913

162.

Hay v. Pfizer, Inc. et al

14-1379

163.

Hayes v. Pfizer, Inc., et al

12-232

164.

Hays v. WKH, et al.

12-233

165.

Heronimus v. Wyeth, Inc et al.

12-2654

166.

Hill v. Pfizer, Inc.

12-3186

167.

Holley v. Pfizer Inc.,et al.

13-2738

168.

Hopkins v. Pfizer et al

12-2218

169.

Howard v. Pfizer, Inc. et al

13-7602

170.

Howard v. Pfizer, Inc.

13-136

171.

Hubbard et al v. Pfizer, Inc. et al

14-720

172.

Hux v. Pfizer Inc. et al

15-2653

8

173.

Iwasyszyn v. Pfizer, Inc.

12-4656

174.

J.G. v. Pfizer Inc., et al.

12-2793

175.

J.M. et al v. Pfizer, Inc. et al

15-875

176.

Jackson v. Pfizer, Inc, et al.

13-2739

177.

Jackson v. Pfizer, Inc., et al

12-1547

178.

Jacobson et al v. Pfizer, Inc. et al

13-6318

179.

James v. Pfizer, Inc .., et al.

13-2736

180.

Janssen v. Pfizer Inc. et al.

13-5943

181.

Jay v. Pfizer, Inc.

12-4580

182.

Jensen v. Pfizer, Inc.

13-282

183.

Jimena v. Pfizer, Inc., et al.

12-2935

184.

Johns v. Pfizer, Inc., et al.

13-3815

185.

Johnson v. Pfizer, Inc.

13-1652

186.

Johnson v. Pfizer, Inc., et al.

12-5779

187.

Johnson v. Pfizer, Inc.

13-4786

188.

Johnson v. Pfizer Inc.

13-7514

189.

Johnson v. Pfizer Inc.

15-314

190.

Johnston v. WMH, et al.

12-2684

191.

Jones-Hawkins v. Pfizer Inc., et al

13-3233

192.

Jones v. Pfizer, Inc.

14-280

193.

Jones v. Pfizer, Inc.

14-281

194.

Juarez v. Pfizer, Inc.

13-5124

195.

Karg v. Pfizer, Inc.

13-446

196.

Kercher v. Pfizer Inc.

13-2282

197.

Keyser v. Pfizer, Inc. et al

13-981

198.

Khampeth v. Pfizer Inc. et al

14-3364

199.

Kinslow v. Pfizer, Inc. et al

13-901

200.

Knight v. WKH, et al.

12-222

201.

Kyle v. Pfizer, Inc., et al.

13-1663

9

202.

Lands v. Pfizer, Inc. et al

14-0719

203.

Lane v. Pfizer, Inc., et al.

13-3644

204.

Daronco v. Pfizer, Inc., et al.

13-4366

205.

Malouin v. Pfizer, Inc., et al.

13-4366

206.

Leaders v. Pfizer, Inc.

13-7535

207.

Leal v. Greenst. & Pfizer Inc.

13-2278

208.

Lee et al v. Pfizer Inc. et al

14-745

209.

Lehew v. Pfizer Inc.

13-2284

210.

Lentz v. Pfizer, Inc., et al.

12-5121

211.

Silva v. Pfizer, Inc., et al.

12-5121

212.

Stephens-Grega! v. Pfizer, Inc., et al.

12-5121

213.

Tinsley v. Pfizer, Inc., et al.

12-5121

214.

Watts v. Pfizer, Inc., et al.

12-5121

215.

Williamson v. Pfizer, Inc., et al.

12-5121

216.

Leos et al. v. Pfizer Inc. et al.

14-605

217.

Letizia-Reichley v. Pfizer Inc.

13-7525

218.

Levada v. Pfizer Inc.

13-2285

219.

Levitt v. Pfizer Inc., et al.

13-2717

220.

Lewis v. Pfizer Inc.

12-2127

221.

Lewis v. Pfizer, Inc.

12-6871

222.

Lexa v. Pfizer, Inc., et al.

13-1735

223.

Lietdke-Hoar v. Pfizer, et al

13-2926

224.

Lilly v. Pfizer, Inc., et al

14-2157

225.

Little v. Pfizer,Inc., et al.

13-880

226.

Logan v. Pfizer, Inc., et al.

13-3603

227.

Long v. WKH, et al.

12-2595

228.

Long et al v. Pfizer, Inc.

14-3061

229.

Long v. Pfizer Inc.

11-3198

230.

Lowes v. Pfizer Inc., et al.

12-2437

10

231.

Lund v. Pfizer Inc. et al

12-6549

232.

M.K.K. v. WKH, Inc., et al.

13-4653

233.

Madison v. Pfizer, Inc.

12-3097

234.

Malacara v. Pfizer Inc., et al

13-2716

235.

Malek v. Pfizer, Inc. et al

13-2197

236.

Mallory v. Pfizer, Inc.

12-2318

237.

Mangine v. Pfizer, Inc., et al.

13-2720

238.

Mapp v. WKH, et al.

12-235

239.

Martin v. Pfizer Inc et al

14-2520

240.

Martin v. Pfizer Inc.

12-652

241.

McClendon v. Pfizer, Inc.

15-569

242.

McFarland v. Pfizer, Inc.

13-2978

243.

McGoun v. WKH, et al.

12-2683

244.

McKee v. Pfizer Inc. et al

13-137

245.

McMonagle v. Pfzier, Inc., et al.

13-877

246.

McMonagle v. Pfzier, Inc., et al.

13-878

247.

Mercier v. Pfizer, Inc. et al.

12-3685

248.

Metcalfv. Pfizer Inc.

13-7521

249.

Miller v. Pfizer, Inc.

13-1729

250.

Miller v. Pfizer, Inc.

14-2802

251.

Mills v. Greenstone, LLC

14-1538

252.

Minichini et al v. Pfizer, Inc. et al

13-6491

253.

Mirones v. Pfizer Inc., et al.

13-4798

254.

Mitchell v. WKH, et al.

12-2688

255.

Mitchell et al v. Pfizer, Inc.

12-2446

256.

Moore v. Greenstone LLC

13-4176

257.

Moore v. WKH, et al.

12-239

258.

Moore v. Pfizer. Inc., et al.

13-4112

259.

Moore v. WKH, et al.

12-2711

11

260.

Muhammad v. Pfizer, Inc.

13-1921

261.

Myers et al v. Pfizer Inc. et al

14-2341

262.

N.A. v. Pfizer, Inc. et al

13-4365

263.

Natola v. Pfizer Inc et al

14-2620

264.

Nixon v. Pfizer Inc. et al

13-138

265.

Nolan v. Pfizer, Inc. et al

12-6665

266.

Noriega v. Pfizer, Inc., et. Al

12-2798

267.

Orellana et al. v. Pfizer Inc.

14-337

268.

Otts et al. v. Pfizer Inc. et al.

14-658

269.

Paglia v. Pfizer, Inc.

15-2018

270.

Paprocki v. Pfizer, Inc., et al.

12-5315

271.

Parker v. Pfizer, Inc.

12-2447

272.

Parsley v. WKH, et al

12-240

273.

Patti v. WKH, et al.

12-2751

274.

Paylor v. Pfizer, Inc., et al.

12-4646

275.

Pazzo v. Pfizer Inc., et al.

13-1362

276.

Peska v. Pfizer Inc.

12-2129

277.

Peterlinz v. Pfizer Inc.

13-7512

278.

Peterson v. Pfizer Inc., et al.

13-53

279.

Petrosky v. Pfizer Inc.

13-2281

280.

Phelps v. Pfizer Inc.

12-2128

281.

Phillips v. Pfizer, Inc. et al

14-0715

282.

Posey v. Pfizer, Inc.

13-2280

283.

Potts v. WKH, et al.

12-241

284.

Prosser v. Pfizer, Inc.

12-3095

285.

Puffer v. Pfizer Inc. et al

13-165

286.

Pulcifer v. Pfizer Inc., et al.

13-2718

287.

Ransdell v. Pfizer Inc. et al

14-5861

288.

Ratliffv. Pfizer

13-4685

12

289.

Rees v. Pfizer Inc. et al.

13-5944

290.

Reese v. Pfizer, Inc

12-4215

291.

Remsburg v. Pfizer, Inc.

12-5962

292.

Richardson v. Pfizer Inc. et al

15-2654

293.

Richburg-Rodriguez v. WKH et al.

12-245

294.

Richmond v. Pfizer Inc. et al

15-1677

295.

Ridler v. Pfizer, Inc.

13-3167

296.

Riviera-Thuener v. Pfizer, Inc. et al

14-5010

297.

Rogers v. Pfizer, Inc.

13-1651

298.

Rogers v. Pfizer, Inc. et al

13-4490

299.

Rorick v. Pfizer, Inc., et al.

12-4089

300.

Rosen v. Pfizer, Inc., et al.

12-2794

301.

Rosenberg v. Pfizer, Inc., et al.

13-2288

302.

Rosenkranz v. WKH, et al.

12-246

303.

Rousseau v. Pfizer Inc., et al.

12-2440

304.

Rowan v. WKH, et al.

12-248

305.

Russo v. Pfizer Inc. et al

13-140

306.

S.C.R. et al v. Pfizer, Inc. et al

14-2185

307.

Saville v. Pfizer, Inc. et al

13-166

308.

Schlomer et al v. Pfizer, Inc. et al

14-2798

309.

Schroeder v. Pfizer, Inc.

13-342

310.

Schulte v. WKH, et al.

12-2752

311.

Schulze v. Pfizer, Inc.

14-284

312.

Scordo v. Pfizer Inc. et al.

14-563

313.

Seaton v. Pfizer, Inc.

13-4113

314.

Sehorn v. Pfizer, Inc.

14-3702

315.

Serrano v. Pfizer, Inc., et al.

12-2909

316.

Shannon et al v. Pfizer Inc. et al

14-1112

317.

Silva v. Pfizer, Inc. et al.

13-2719

13

318.

Simmons v. Pfizer, Inc., et al.

13-817

319.

Simpson v. WKH, et al.

12-2755

320.

Slove v. Pfizer, Inc. et al

14-718

321.

Smith v. Pfizer, Inc. et al

14-912

322.

SRD et al v. Pfizer, Inc et al

15-1526

323.

St. Pierre v. Pfizer, Inc., et al.

12-6079

324.

Starr v. Pfizer, In.c

13-1322

325.

Stengrund v. Pfizer Inc. et al

14-2343

326.

Stephens et al v. Pfizer, Inc. et al

14-1262

327.

Stephens v. Pfizer, Inc., et al.

13-2401

328.

Stevens v. Pfizer, Inc. et al

14-2843

329.

Stewart v. Pfizer, Inc., et al.

12-2797

330.

Stewart v. Pfizer, Inc. et al

14-4216

331.

Stice v. Pfizer Inc.

13-7520

332.

Sturman v. Pfizer Inc.

13-52

333.

Swartz v. Pfizer, Inc.

13-308

334.

Sweigart v. Pfizer, Inc. et al

15-261

335.

Swift v. Pfizer Inc.

13-2976

336.

Swindler v. Pfizer, Inc. et al

13-1287

337.

Cameron v. Pfizer, Inc., et al

12-5123

338.

Rutledge v. Pfizer, Inc., et al.

12-5123

339.

Teach v. Pfizer Inc., et a.

13-2724

340.

Tebo v. Pfizer, Inc., et al.

13-2292

341.

Teed v. Pfizer Inc., et al.

13-139

342.

Thomas v. Pfizer, Inc. et al

14-717

343.

Thompson v. Pfizer Inc et al.

13-5727

344.

Thompson et al v. Pfizer, Inc. et al

14-914

345.

Thornton v. Pfizer, Inc.

13-4110

346.

Tinsley v. Pfizer, Inc.

13-3953

14

347.

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14-3367

348.

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13-2754

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351.

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14-3701

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13-2455

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12-2439

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12-2712

369.

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13-141

370.

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372.

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14-910

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12-4088

375.

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12-2710

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376.

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12-5616

377.

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13-2729

378.

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13-1270

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380.

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13-2402

381.

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12-252

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Zuwala v. Pfizer, Inc.

12-3096

16