School on Small Associations Junk Epidemiology II

The EPA is making their whole case with small particle air pollution claims that are based in Epidemiological techniques.
Unfortunately epidemiology is problematic and deceptive as used by the EPA researchers.

The Claims of the US EPA sponsored and supported researchers are that hundreds of thousands of Americans, millions of humans on the planet are dying every year from small particle air pollution. The claims are based on geographical studies that study populations in what is called an observational (ecological) study of events.
Their small paricle claims will be the basis of outrageous and unreasonable regulatory regimes, if allowed to stand.
EPA funded and supporte research studies on small particle air pollution human effect are not medical investigations, they are not medical at all, they are just counting exercises.
The researchers on small particles events just compare of outside air pollution monitor information to deaths in the area of the monitor.
The endpoint that they claim shows a toxic effect is an increase of death rate over normal or expected. Expected is average daily death rate. Deaths counted are all deaths that are non trauma, non cancer, non renal failure deaths.
Small (also called fine) particles are 2.5 microns in diameter, and considered toxic because they can get way down into the lungs.
10 microns is the width of a human hair.
1. Now there is no disease or medical condition identified with small particles, but the assumption by the EPA and its researchers is they must be bad.
2. The researchers know that small particles vary from dust to industrial to internal combustion engine emission particles. Small particle only identifies the size, not the chemical composition of the particle. The EPA marches on, and disregards that little problem. As an extreme example to emphasize how silly that is, talcum powder and weaponized anthrax would be small particle air pollutants by the US EPA standards for research.
3. Then the studies measure small particle pollution for an area and compare with death records, eliminating trauma and cancer deaths adn counting deaths in excess of expected.
4. They make no effort to adjust for inside versus outside exposure, and use outside monitoring even though people spend 90 percent of their time indoors (it could be worse or better, depending on housing quality and cooking methods, for example, with some inside pollution worse, some less).
And the deaths that are important are called premature deaths byt the EPA researchers, but in fact they count excess deaths because premature deaths can only be identified by individual actuarial and medical assessment of life expectancy and a death that occurs before life expectancy. In the case of excess deaths they just count the deaths higher than the average daily rate. What could be easier?
So there we have it, and the many air pollution studies that are used to project hundreds of thousands of deaths from small particles invariably have Relative Risk which is Risk of the exposed population for the endpoint in excess of the control population which is set at 0 and has a Relative Risk of 1.0 arbitrarily moved to 1.0 for epidemiology studies. If a rate of exposed disease or death is less than 1.0 it means the exposure has a protective or beneficial effect, a positive number above 1.0 is a negative toxic effect, in this case increased rate of death, from exposure to small particles.
The study picks a lag time from an increase in air pollution and looks for excess deaths. The US EPA researchers pick a lag time of more than one day, less than 5 days as a rule from what I have seeen–this choice is not based on any physiopathological analysis, these research projects have not idea about mechanism that might cause death so they are doing monkey doodling to decide how much lag time to use.
Then the excess deaths is a percentage of the expected, so if for the study population/area the prepoerly categorized deaths are expected to be 100, 105 deaths in the same category for the day would be a Relative Risk of 1.05. Many of the air pollution studies done by the US EPA are in the range of 1.05 to 1.1, some slightly higher some slightly lower. I have not seen many that exceeded 1.20.
The Relative Riske is put up and also includes a Confidence interval, that is another important epidemiological measure of what you might call range of reliability. So that means something in the US EPA studies because many come very close to including a no effect in the confidence interval. Confidence interval is a calculated range of reliability for the Relative Risk, so, for example the RR is reported at 1.06 with a confidence interval (CI) of range to assure compliance with statistical significance of 95 % confidence interval (which means a P value of 0.05 or a 1 in 20 rate of error)
If the confidence interval has 1.01 as it’s lower range it is just barely enough to save the study with it’s RR of 1.06.
If the Confidence Interval includes 1.0, the study has just been declared as null, with results that included 1.0 or no association.
So the RR is a range, and the range is defined by the Confidence Interval, and if the Confidence interval includes 1.0 there is no evidenc of an effect at all positive or negative.
REVIEW THIS MATERIAL IF YOU NEED TO BECAUSE THE NEXT PART SHOWS THE STRAIGHT FORWARD DISCUSSION BY THE EPIDEMIOLOGISTS ON WHY IT IS NOT REASONABLE TO ACCEPT SMALL ASSOCIATIONS SUCH AS A RELATIVE RISK OF 1.1 IN FACT A RELATIVE RISK UP TO 1.9 AS RELIABLE EVIDENCE FOR PROOF OF CAUSE OF EFFECT IN ONE OF THESE STUDIES.
From the 2nd and 3rd editions of the Reference Manual on Scientific Evidence published by the Federal Judicial Center, the educational organization for Federal Judges. 2nd edition published 2000, 3rd ed. published 2011.
The pertinent sections from the epidemiology chapters of these manuals that address the magnitude of the Relative Risk required to create reliable evidence of cause of toxic effect in an observational epidemiological study.
The full text of both books is available for free at the web site for the Federal Judicial Center. http://www.fjc.gov
teh 3rd edition is available at the National Academies Press.
A PDF file of this abbreviated excerpting from the books is here:

Click to access 2nd-and-3rd-epi-highlights-ref-manual.pdf

Reference Manual on
Scientific Evidence
Second Edition
Federal Judicial Center 2000
This Federal Judicial Center publication was undertaken in furtherance of the Center’s
statutory mission to develop and conduct education programs for judicial branch em-
ployees. The views expressed are those of the authors and not necessarily those of the
Federal Judicial Center.
An electronic version of the Reference Manual can be downloaded from the
Federal Judicial Center’s site on the World Wide Web. Go to
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For the Center’s overall homepage on the Web, go to
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333 Reference Guide on Epidemiology,
Michael D. Green, D. Mical Freedman & Leon Gordis
v
Preface
Thomas Henry Huxley observed that “science is simply common sense at its
best; that is, rigidly accurate in observation and merciless to a fallacy in logic.”1
This second edition of the Reference Manual on Scientific Evidence furthers the goal
of assisting federal judges in recognizing the characteristics and reasoning of
“science” as it is relevant in litigation.
The following sections of this reference guide address a number of critical
issues that arise in considering the admissibility of, and weight to be accorded to,
epidemiologic research findings. Over the past couple of decades, courts fre-
quently have confronted the use of epidemiologic studies as evidence and rec-
ognized their utility in proving causation. As the Third Circuit observed in
DeLuca v. Merrell Dow Pharmaceuticals, Inc.: “The reliability of expert testimony
founded on reasoning from epidemiological data is generally a fit subject for
judicial notice; epidemiology is a well-established branch of science and medi-
cine, and epidemiological evidence has been accepted in numerous cases.”12
Three basic issues arise when epidemiology is used in legal disputes and the
methodological soundness of a study and its implications for resolution of the
question of causation must be assessed:
1. Do the results of an epidemiologic study reveal an association between an
agent and disease?
2. What sources of error in the study may have contributed to an inaccurate
result?
3. If the agent is associated with disease, is the relationship causal?
Reference Guide on Epidemiology
339
When an agent’s effects are suspected to be harmful, we cannot knowingly
expose people to the agent. 14 Instead of the investigator controlling who is
exposed to the agent and who is not, most epidemiologic studies are observa-
tional—that is, they “observe” a group of individuals who have been exposed to
an agent of interest, such as cigarette smoking or an industrial chemical, and
compare them with another group of individuals who have not been so ex-
posed.
14.Experimental studies in which human beings are exposed to agents known or thought to be toxic are ethically proscribed. See Ethyl Corp. v. United States Envtl. Protection Agency, 541 F.2d 1,
Reference Manual on Scientific Evidence
340
The difference between cohort studies and case-control studies is that cohort
studies measure and compare the incidence of disease in the exposed and unex-
posed (“control”) groups, while case-control studies measure and compare the
frequency of exposure in the group with the disease (the “cases”) and the group
without the disease (the “controls”). Thus, a cohort study takes the exposed
status of participants (the independent variable) and examines its effect on inci-
dence of disease (the dependent variable). A case-control study takes the disease
status as the independent variable and examines its relationship with exposure,
which is the dependent variable.
III. How Should Results of an Epidemiologic
Study Be Interpreted?
Epidemiologists are ultimately interested in whether a causal relationship exists
between an agent and a disease. However, the first question an epidemiologist
addresses is whether an association exists between exposure to the agent and
disease. An association between exposure to an agent and disease exists when
they occur together more frequently than one would expect by chance. 42 Al-
though a causal relationship is one possible explanation for an observed associa-
tion between an exposure and a disease, an association does not necessarily mean
that there is a cause–effect relationship. Interpreting the meaning of an observed
association is discussed below.
This section begins by describing the ways of expressing the existence and
strength of an association between exposure and disease. It reviews ways in
which an incorrect result can be produced because of the sampling methods
used in all observational epidemiologic studies and then examines statistical
methods for evaluating whether an association is real or due to sampling error.
The strength of an association between exposure and disease can be stated as
a relative risk, an odds ratio, or an attributable risk (often abbreviated as “RR,”
“OR,” and “AR,” respectively). Each of these measurements of association
examines the degree to which the risk of disease increases when individuals are
exposed to an agent.
A. Relative Risk
A commonly used approach for expressing the association between an agent and
disease is relative risk (RR). It is defined as the ratio of the incidence rate (often
referred to as incidence) of disease in exposed individuals to the incidence rate
in unexposed individuals:
Relative Risk (RR) = Incidence rate in the exposed
Incidence rate in the unexposed
The incidence rate of disease reflects the number of cases of disease that
develop during a specified period of time divided by the number of persons in
the cohort under study. 43 Thus, the incidence rate expresses the risk that a
42. A negative association implies that the agent has a protective or curative effect. Because the
concern in toxic substances litigation is whether an agent caused disease, this reference guide focuses on
positive associations.
 The relative risk is calculated as the incidence rate in the exposed group
(0.4) divided by the incidence rate in the unexposed group (0.1), or 4.0.
A relative risk of 4.0 indicates that the risk of disease in the exposed group is four
times as high as the risk of disease in the unexposed group. 44
In general, the relative risk can be interpreted as follows:
 If the relative risk equals 1.0, the risk in exposed individuals is the same as
the risk in unexposed individuals. There is no association between exposure
to the agent and disease.
 If the relative risk is greater than 1.0, the risk in exposed individuals is
greater than the risk in unexposed individuals. There is a positive associa-
tion between exposure to the agent and the disease, which could be causal.
 If the relative risk is less than 1.0, the risk in exposed individuals is less than
the risk in unexposed individuals. There is a negative association, which
could reflect a protective or curative effect of the agent on risk of disease.
For example, immunizations lower the risk of disease. The results suggest
that immunization is associated with a decrease in disease and may have a
protective effect on the risk of disease.
Although relative risk is a straightforward concept, care must be taken in
interpreting it. Researchers should scrutinize their results for error. Error in the
design of a study could yield an incorrect relative risk. Sources of bias and con-
founding should be examined. 45 Whenever an association is uncovered, further
analysis should be conducted to determine if the association is real or due to an
error or bias. Similarly, a study that does not find an association between an
agent and disease may be erroneous because of bias or random error.
362
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362
3. Power
When a study fails to find a statistically significant association, an important
question is whether the result tends to exonerate the agent’s toxicity or is essen-
tially inconclusive with regard to toxicity. The concept of power can be helpful
in evaluating whether a study’s outcome is exonerative or inconclusive. 79
The power of a study expresses the probability of finding a statistically signifi-
cant association of a given magnitude (if it exists) in light of the sample sizes used
in the study.
The power of a study is the complement of beta (1 – ). Thus, a study with
a likelihood of .25 of failing to detect a true relative risk of 2.0 82 or greater has a
power of .75. This means the study has a 75% chance of detecting a true relative
risk of 2.0. If the power of a negative study to find a relative risk of 2.0 or greater
control groups in different studies in which some gave the controls a placebo and others gave the
controls an alternative treatment), cert. denied, 510 U.S. 914 (1993).
82. We use a relative risk of 2.0 for illustrative purposes because of the legal significance some
courts have attributed to this magnitude of association. See infra § VII.
epidemiology cannot objectively prove causation;
rather, causation is a judgment for epidemiologists and others interpreting the
epidemiologic data. Moreover, scientific determinations of causation are inher-
ently tentative. The scientific enterprise must always remain open to reassessing
the validity of past judgments as new evidence develops.
In assessing causation, researchers first look for alternative explanations for
the association, such as bias or confounding factors, which were discussed in
section IV. Once this process is completed, researchers consider how guidelines
Reference Guide on Epidemiology
375
for inferring causation from an association apply to the available evidence. These
guidelines consist of several key inquiries that assist researchers in making a
judgment about causation. 110 Most researchers are conservative when it comes
to assessing causal relationships, often calling for stronger evidence and more
research before a conclusion of causation is drawn. 111
The factors that guide epidemiologists in making judgments about causation
are
1. temporal relationship;
2. strength of the association;
3. dose–response relationship;
4. replication of the findings;
5. biological plausibility (coherence with existing knowledge);
6. consideration of alternative explanations;
7. cessation of exposure;
8. specificity of the association; and
9. consistency with other knowledge.
There is no formula or algorithm that can be used to assess whether a causal
inference is appropriate based on these guidelines. One or more factors may be
absent even when a true causal relationship exists. Similarly, the existence of
some factors does not ensure that a causal relationship exists. Drawing causal
inferences after finding an association and considering these factors requires judg-
ment and searching analysis, based on biology, of why a factor or factors may be
absent despite a causal relationship, and vice-versa. While the drawing of causal
inferences is informed by scientific expertise, it is not a determination that is
made by using scientific methodology.
Reference Manual on Scientific Evidence
376
These guidelines reflect criteria proposed by the U.S. Surgeon General in
1964 112 in assessing the relationship between smoking and lung cancer and ex-
panded upon by A. Bradford Hill in 1965. 113
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384
it is recognized as a cause of that disease in general.”137 The following discussion
should be read with this caveat in mind. 138
The threshold for concluding that an agent was more likely than not the
cause of an individual’s disease is a relative risk greater than 2.0. Recall that a
relative risk of 1.0 means that the agent has no effect on the incidence of disease.
When the relative risk reaches 2.0, the agent is responsible for an equal number
of cases of disease as all other background causes. Thus, a relative risk of 2.0
(with certain qualifications noted below) implies a 50% likelihood that an ex-
posed individual’s disease was caused by the agent. A relative risk greater than
2.0 would permit an inference that an individual plaintiff’s disease was more
likely than not caused by the implicated agent. 139 A substantial number of courts
in a variety of toxic substances cases have accepted this reasoning. 140
also Steve Gold, Note, Causation in Toxic Torts: Burdens of Proof, Standards of Persuasion and Statistical

138. We emphasize this caveat, both because it is not intuitive and because some courts have failed
to appreciate the difference between an association and a causal relationship. See, e.g., Forsyth v. Eli
Lilly & Co., Civ. No. 95-00185 ACK, 1998 U.S. Dist. LEXIS 541, at *26–*31 (D. Haw. Jan. 5, 1998).
But see Berry v. CSX Transp., Inc., 709 So. 2d 552, 568 (Fla. Dist. Ct. App. 1998) (“From epidemio-
logical studies demonstrating an association, an epidemiologist may or may not infer that a causal rela-
tionship exists.”).
This PDF is available from The National Academies Press at http://www.nap.edu/catalog.php?record_id=13163
relative risk of 1.0 means that the agent has no effect on the incidence of disease.
When the relative risk reaches 2.0, the agent is responsible for an equal number
of cases of disease as all other background causes. Thus, a relative risk of 2.0
(with certain qualifications noted below) implies a 50% likelihood that an ex-
posed individual’s disease was caused by the agent. A relative risk greater than
2.0 would permit an inference that an individual plaintiff’s disease was more
likely than not caused by the implicated agent. 139 A substantial number of courts
in a variety of toxic substances cases have accepted this reasoning.

Reference Guide on
Epidemiology 3rd Ed.
http://www.nap.edu/catalog.php?record_id=13163
miChael d. Green, d. miChal freedman, and leon Gordis
Michael D. Green, J.D., is Bess & Walter Williams Chair in Law, Wake Forest University
School of Law, Winston-Salem, North Carolina.
D. Michal Freedman, J.D., Ph.D., M.P.H., is Epidemiologist, Division of Cancer Epide- miology and Genetics, National Cancer Institute, Bethesda, Maryland.
Leon Gordis, M.D., M.P.H., Dr.P.H., is Professor Emeritus of Epidemiology, Johns Hopkins Bloomberg School of Public Health, and Professor Emeritus of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland.
C o n T e n T s
I. Introduction, 551
II. What Different Kinds of Epidemiologic Studies Exist? 555
A. Experimental and Observational Studies of Suspected Toxic
Agents, 555
B. Types of Observational Study Design, 556
1. Cohort studies, 557
2. Case-control studies, 559
3. Cross-sectional studies, 560
4. Ecological studies, 561
C. Epidemiologic and Toxicologic Studies, 563
III. How Should Results of an Epidemiologic Study Be Interpreted? 566
A. Relative Risk, 566
B. Odds Ratio, 568
C. Attributable Risk, 570
1. False positives and statistical significance, 575
2. False negatives, 581
3. Power, 582
549
Reference Manual on Scientific Evidence
V. General Causation: Is an Exposure a Cause of the Disease? 597
A. Is There a Temporal Relationship? 601
B. How Strong Is the Association Between the Exposure and
Disease? 602
C. Is There a Dose–Response Relationship? 603
D. Have the Results Been Replicated? 604
E. Is the Association Biologically Plausible (Consistent with Existing
Knowledge)? 604
F. Have Alternative Explanations Been Considered? 605
G. What Is the Effect of Ceasing Exposure? 605
H. Does the Association Exhibit Specificity? 605
I. Are the Findings Consistent with Other Relevant Knowledge? 606
VI. What Methods Exist for Combining the Results of Multiple Studies? 606
VII. What Role Does Epidemiology Play in Proving Specific Causation? 608
550
V. General Causation: Is an Exposure a
Cause of the Disease?
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. When epidemiologists evaluate whether a cause–effect relationship exists between an agent and disease, they are using the term causation in a way similar to, but not identical to, the way that the familiar “but for,” or sine qua non, test is used in law for cause in fact. “Conduct is a factual cause of
135. For a more complete discussion of multivariate analysis, see Daniel L. Rubinfeld, Reference
Guide on Multiple Regression, in this manual.
597
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[harm] when the harm would not have occurred absent the conduct.”136 This is equivalent to describing the conduct as a necessary link in a chain of events that results in the particular event.137 Epidemiologists use causation to mean that an increase in the incidence of disease among the exposed subjects would not have occurred had they not been exposed to the agent.138 Thus, exposure is a necessary condition for the increase in the incidence of disease among those exposed.139
The relationship between the epidemiologic concept of cause and the legal ques- tion of whether exposure to an agent caused an individual’s disease is addressed in Section VII.
As mentioned in Section I, epidemiology cannot prove causation; rather, causation is a judgment for epidemiologists and others interpreting the epidemiologic data.140 Moreover, scientific determinations of causation are inherently tentative. The scientific enterprise must always remain open to reassessing the validity of past judgments as new evidence develops.
In assessing causation, researchers first look for alternative explanations for the association, such as bias or confounding factors, which are discussed in Section IV, supra. Once this process is completed, researchers consider how guidelines for inferring causation from an association apply to the available evidence. We emphasize that these guidelines are employed only after a study finds an association
139. See Rothman et al., supra note 61, at 8 (“We can define a cause of a specific disease event as an antecedent event, condition, or characteristic that was necessary for the occurrence of the disease at the moment it occurred, given that other conditions are fixed.”); Allen v. United States, 588 F. Supp.
247, 405 (D. Utah 1984) (quoting a physician on the meaning of the statement that radiation causes cancer), rev’d on other grounds, 816 F.2d 1417 (10th Cir. 1987).
140. Restatement (Third) of Torts: Liability for Physical and Emotional Harm § 28 cmt. c (2010) (“[A]n evaluation of data and scientific evidence to determine whether an inference of causation is appropriate requires judgment and interpretation.”).
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to determine whether that association reflects a true causal relationship.141 These guidelines consist of several key inquiries that assist researchers in making a judg- ment about causation.142 Generally, researchers are conservative when it comes to assessing causal relationships, often calling for stronger evidence and more research before a conclusion of causation is drawn.143
678–79 (M.D.N.C. 2003) (“The greater weight of authority supports Sandoz’ assertion that [use of] the Bradford Hill criteria is a method for determining whether the results of an epidemiologic study can be said to demonstrate causation and not a method for testing an unproven hypothesis.”); Soldo,
244 F. Supp. 2d at 514 (the Hill criteria “were developed as a mean[s] of interpreting an established association based on a body of epidemiologic research for the purpose of trying to judge whether the observed association reflects a causal relation between an exposure and disease.” (quoting report of court-appointed expert)).
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The factors that guide epidemiologists in making judgments about causation
(and there is no threshold number that must exist) are
1. Temporal relationship,
2. Strength of the association,
3. Dose–response relationship,
4. Replication of the findings,
5. Biological plausibility (coherence with existing knowledge),
6. Consideration of alternative explanations,
7. Cessation of exposure,
8. Specificity of the association, and
9. Consistency with other knowledge.
There is no formula or algorithm that can be used to assess whether a causal inference is appropriate based on these guidelines.145 One or more factors may be absent even when a true causal relationship exists.146 Similarly, the existence of some factors does not ensure that a causal relationship exists. Drawing causal inferences after finding an association and considering these factors requires judg- ment and searching analysis, based on biology, of why a factor or factors may be absent despite a causal relationship, and vice versa. Although the drawing of causal inferences is informed by scientific expertise, it is not a determination that is made by using an objective or algorithmic methodology.
These guidelines reflect criteria proposed by the U.S. Surgeon General in 1964147 in assessing the relationship between smoking and lung cancer and expanded upon by Sir Austin Bradford Hill in 1965148 and are often referred to as the Hill criteria or Hill factors.
146. See Cook v. Rockwell Int’l Corp., 580 F. Supp. 2d 1071, 1098 (D. Colo. 2006) (rejecting argument that plaintiff failed to provide sufficient evidence of causation based on failing to meet four of the Hill factors).
148. See Austin Bradford Hill, The Environment and Disease: Association or Causation? 58 Proc. Royal Soc’y Med. 295 (1965) (Hill acknowledged that his factors could only serve to assist in the infer- ential process: “None of my nine viewpoints can bring indisputable evidence for or against the cause- and-effect hypothesis and none can be required as a sine qua non.”). For discussion of these criteria and their respective strengths in informing a causal inference, see Gordis, supra note 32, at 236–39; David E. Lilienfeld & Paul D. Stolley, Foundations of Epidemiology 263–66 (3d ed. 1994); Weed, supra note 144.
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A. Is There a Temporal Relationship?
A temporal, or chronological, relationship must exist for causation to exist. If an exposure causes disease, the exposure must occur before the disease develops.
Reference Manual on Scientific Evidence
B. How Strong Is the Association Between the Exposure and
Disease?155
The relative risk is one of the cornerstones for causal inferences.156 Relative risk measures the strength of the association. The higher the relative risk, the greater the likelihood that the relationship is causal.157 For cigarette smoking, for example, the estimated relative risk for lung cancer is very high, about 10.158 That is, the risk of lung cancer in smokers is approximately 10 times the risk in nonsmokers.
A relative risk of 10, as seen with smoking and lung cancer, is so high that it is extremely difficult to imagine any bias or confounding factor that might account for it. The higher the relative risk, the stronger the association and the lower the chance that the effect is spurious. Although lower relative risks can reflect causality, the epidemiologist will scrutinize such associations more closely because there is a greater chance that they are the result of uncontrolled con- founding or biases.
155. Assuming that an association is determined to be causal, the strength of the association plays an important role legally in determining the specific causation question—whether the agent caused an individual plaintiff’s injury. See infra Section VII.
156. See supra Section III.A.
157. See Miller v. Pfizer, Inc., 196 F. Supp. 2d 1062, 1079 (D. Kan. 2002) (citing this refer- ence guide); Landrigan v. Celotex Corp., 605 A.2d 1079, 1085 (N.J. 1992). The use of the strength of the association as a factor does not reflect a belief that weaker effects occur less frequently than stronger effects. See Green, supra note 47, at 652–53 n.39. Indeed, the apparent strength of a given agent is dependent on the prevalence of the other necessary elements that must occur with the agent to produce the disease, rather than on some inherent characteristic of the agent itself. See Rothman et al., supra note 61, at 9–11.
158. See Doll & Hill, supra note 6. The relative risk of lung cancer from smoking is a function of intensity and duration of dose (and perhaps other factors). See Karen Leffondré et al., Modeling Smoking History: A Comparison of Different Approaches, 156 Am. J. Epidemiology 813 (2002). The relative risk provided in the text is based on a specified magnitude of cigarette exposure.
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C. Is There a Dose–Response Relationship?
A dose–response relationship means that the greater the exposure, the greater the risk of disease. Generally, higher exposures should increase the incidence (or severity) of disease.159 However, some causal agents do not exhibit a dose– response relationship when, for example, there is a threshold phenomenon (i.e., an exposure may not cause disease until the exposure exceeds a certain dose).160
Thus, a dose–response relationship is strong, but not essential, evidence that the relationship between an agent and disease is causal.161
159. See Newman v. Motorola, Inc., 218 F. Supp. 2d 769, 778 (D. Md. 2002) (recognizing importance of dose–response relationship in assessing causation).
160. The question whether there is a no-effect threshold dose is a controversial one in a variety of toxic substances areas. See, e.g., Irving J. Selikoff, Disability Compensation for Asbestos-Associated Disease in the United States: Report to the U.S. Department of Labor 181–220 (1981); Paul Kotin, Dose–Response Relationships and Threshold Concepts, 271 Ann. N.Y. Acad. Sci. 22 (1976); K. Robock, Based on Available Data, Can We Project an Acceptable Standard for Industrial Use of Asbestos? Absolutely,
330 Ann. N.Y. Acad. Sci. 205 (1979); Ferebee v. Chevron Chem. Co., 736 F.2d 1529, 1536 (D.C. Cir. 1984) (dose–response relationship for low doses is “one of the most sharply contested questions currently being debated in the medical community”); In re TMI Litig. Consol. Proc., 927 F. Supp.
834, 844–45 (M.D. Pa. 1996) (discussing low-dose extrapolation and no-dose effects for radiation exposure).
Moreover, good evidence to support or refute the threshold-dose hypothesis is exceedingly unlikely because of the inability of epidemiology or animal toxicology to ascertain very small effects. Cf. Arnold L. Brown, The Meaning of Risk Assessment, 37 Oncology 302, 303 (1980). Even the shape of the dose–response curve—whether linear or curvilinear, and if the latter, the shape of the curve—is a matter of hypothesis and speculation. See Allen v. United States, 588 F. Supp. 247, 419–24 (D. Utah
The idea that the “dose makes the poison” is a central tenet of toxicology and attributed to Paracelsus, in the sixteenth century. See Bernard D. Goldstein & Mary Sue Henifin, Reference Guide on Toxicology, Section I.A, in this manual. It does not mean that any agent is capable of causing any disease if an individual is exposed to a sufficient dose. Agents tend to have specific effects, see infra Section V.H., and this dictum reflects only the idea that there is a safe dose below which an agent does not cause any toxic effect. See Michael A Gallo, History and Scope of Toxicology, in Casarett and Doull’s Toxicology: The Basic Science of Poisons 1, 4–5 (Curtis D. Klaassen ed., 7th ed. 2008). For a case in which a party made such a mistaken interpretation of Paracelsus, see Alder v. Bayer Corp., AGFA Div., 61 P.3d 1068, 1088 (Utah 2002). Paracelsus was also responsible for the initial articulation of the specificity tenet. See infra Section V.H.
161. Evidence of a dose–response relationship as bearing on whether an inference of general causation is justified is analytically distinct from determining whether evidence of the dose to which a plaintiff was exposed is required in order to establish specific causation. On the latter matter, see infra Section VII; Restatement (Third) of Torts: Liability for Physical and Emotional Harm § 28 cmt. c(2) & rptrs. note (2010).
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D. Have the Results Been Replicated?
Rarely, if ever, does a single study persuasively demonstrate a cause–effect rela- tionship.162 It is important that a study be replicated in different populations and by different investigators before a causal relationship is accepted by epidemiologists and other scientists.163
The need to replicate research findings permeates most fields of science. In epidemiology, research findings often are replicated in different populations.164
Consistency in these findings is an important factor in making a judgment about causation. Different studies that examine the same exposure–disease relationship generally should yield similar results. Although inconsistent results do not neces- sarily rule out a causal nexus, any inconsistencies signal a need to explore whether different results can be reconciled with causality.
E. Is the Association Biologically Plausible (Consistent with
Existing Knowledge)?165
Biological plausibility is not an easy criterion to use and depends upon existing knowledge about the mechanisms by which the disease develops. When biologi- cal plausibility exists, it lends credence to an inference of causality. For example, the conclusion that high cholesterol is a cause of coronary heart disease is plausi- ble because cholesterol is found in atherosclerotic plaques. However, observations have been made in epidemiologic studies that were not biologically plausible at the time but subsequently were shown to be correct.166 When an observation is inconsistent with current biological knowledge, it should not be discarded, but

162. In Kehm v. Procter & Gamble Co., 580 F. Supp. 890, 901 (N.D. Iowa 1982), aff’d, 724 F.2d
613 (8th Cir. 1983), the court remarked on the persuasive power of multiple independent studies, each of which reached the same finding of an association between toxic shock syndrome and tampon use.
163. This may not be the legal standard, however. Cf. Smith v. Wyeth-Ayerst Labs. Co., 278
F. Supp. 2d 684, 710 n.55 (W.D.N.C. 2003) (observing that replication is difficult to establish when there is only one study that has been performed at the time of trial).
164. See Cadarian v. Merrell Dow Pharms., Inc., 745 F. Supp. 409, 412 (E.D. Mich. 1989) (holding a study on Bendectin insufficient to support an expert’s opinion, because “the study’s authors themselves concluded that the results could not be interpreted without independent confirmatory evidence”).
165. A number of courts have adverted to this criterion in the course of their discussions of causation in toxic substances cases. E.g., In re Phenylpropanolamine (PPA) Prods. Liab. Litig., 289 F. Supp. 2d 1230, 1247–48 (W.D. Wash. 2003); Cook v. United States, 545 F. Supp. 306, 314–15 (N.D. Cal. 1982) (discussing biological implausibility of a two-peak increase of disease when plotted against time); Landrigan v. Celotex Corp., 605 A.2d 1079, 1085–86 (N.J. 1992) (discussing the existence vel non of biological plausibility); see also Bernard D. Goldstein & Mary Sue Henifin, Reference Guide on Toxicology, Section III.E, in this manual.
166. See In re Rezulin Prods. Liab. Litig., 369 F. Supp. 2d 398, 405 (S.D.N.Y. 2005); In re
Phenylpropanolamine (PPA) Prods. Liab. Litig., 289 F. Supp. 2d 1230, 1247 (W.D. Wash. 2003).
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the observation should be confirmed before significance is attached to it. The saliency of this factor varies depending on the extent of scientific knowledge about the cellular and subcellular mechanisms through which the disease process works. The mechanisms of some diseases are understood quite well based on the available evidence, including from toxicologic research, whereas other mecha- nism explanations are merely hypothesized—although hypotheses are sometimes accepted under this factor.167

F. Have Alternative Explanations Been Considered?
The importance of considering the possibility of bias and confounding and ruling out the possibilities is discussed above.168
G. What Is the Effect of Ceasing Exposure?
If an agent is a cause of a disease, then one would expect that cessation of exposure to that agent ordinarily would reduce the risk of the disease. This has been the case, for example, with cigarette smoking and lung cancer. In many situations, however, relevant data are simply not available regarding the possible effects of ending the exposure. But when such data are available and eliminating exposure reduces the incidence of disease, this factor strongly supports a causal relationship.
H. Does the Association Exhibit Specificity?
An association exhibits specificity if the exposure is associated only with a single disease or type of disease.169 The vast majority of agents do not cause a wide vari-
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I. Are the Findings Consistent with Other Relevant Knowledge?
In addressing the causal relationship of lung cancer to cigarette smoking, research- ers examined trends over time for lung cancer and for cigarette sales in the United States. A marked increase in lung cancer death rates in men was observed, which appeared to follow the increase in sales of cigarettes. Had the increase in lung cancer deaths followed a decrease in cigarette sales, it might have given researchers pause. It would not have precluded a causal inference, but the inconsistency of the trends in cigarette sales and lung cancer mortality would have had to be explained.
VII. What Role Does Epidemiology Play in
Proving Specific Causation?
Epidemiology is concerned with the incidence of disease in populations, and epidemiologic studies do not address the question of the cause of an individual’s disease.178 This question, often referred to as specific causation, is beyond the
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. . . Epidemiology has its limits at the point where an inference is made that the relationship between an agent and a disease is causal (general causation) and where the magnitude of excess risk attributed to the agent has been determined; that is, epidemiologists investigate whether an agent can cause a disease, not whether an agent did cause a specific plaintiff’s disease.179
Nevertheless, the specific causation issue is a necessary legal element in a toxic substance case. The plaintiff must establish not only that the defendant’s agent is capable of causing disease, but also that it did cause the plaintiff’s disease. Thus, numerous cases have confronted the legal question of what is acceptable proof of specific causation and the role that epidemiologic evidence plays in answering that question.180 This question is not a question that is addressed by epidemiology.181 Rather, it is a legal question with which numerous courts
180. In many instances, causation can be established without epidemiologic evidence. When the mechanism of causation is well understood, the causal relationship is well established, or the tim- ing between cause and effect is close, scientific evidence of causation may not be required. This is frequently the situation when the plaintiff suffers traumatic injury rather than disease. This section addresses only those situations in which causation is not evident, and scientific evidence is required.
181. Nevertheless, an epidemiologist may be helpful to the factfinder in answering this question. Some courts have permitted epidemiologists (or those who use epidemiologic methods) to testify about specific causation.
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The remainder of this section is predominantly an explana- tion of judicial opinions. It is, in addition, in its discussion of the reasoning behind applying the risk estimates of an epidemiologic body of evidence to an individual, informed by epidemiologic principles and methodological research.
Before proceeding, one more caveat is in order. This section assumes that epidemiologic evidence has been used as proof of causation for a given plaintiff. The discussion does not address whether a plaintiff must use epidemiologic evi- dence to prove causation.183
Two legal issues arise with regard to the role of epidemiology in proving individual causation: admissibility and sufficiency of evidence to meet the burden of production. The first issue tends to receive less attention by the courts but nevertheless deserves mention. An epidemiologic study that is sufficiently rigor- ous to justify a conclusion that it is scientifically valid should be admissible,184 as it tends to make an issue in dispute more or less likely.185
182. See Restatement (Third) of Torts: Liability for Physical and Emotional Harm § 28 cmt. c(3) (2010) (“Scientists who conduct group studies do not examine specific causation in their research. No scientific methodology exists for assessing specific causation for an individual based on group studies. Nevertheless, courts have reasoned from the preponderance-of-the-evidence standard to determine the sufficiency of scientific evidence on specific causation when group-based studies are involved”).
183. See id. § 28 cmt. c(3) & rptrs. note (“most courts have appropriately declined to impose a threshold requirement that a plaintiff always must prove causation with epidemiologic evidence”); see also Westberry v. Gislaved Gummi AB, 178 F.2d 257 (4th Cir. 1999) (acute response, differential diagnosis ruled out other known causes of disease, dechallenge, rechallenge tests by expert that were consistent with exposure to defendant’s agent causing disease, and absence of epidemiologic or toxi- cologic studies; holding that expert’s testimony on causation was properly admitted); Zuchowicz v. United States, 140 F.3d 381 (2d Cir. 1998); In re Heparin Prods. Liab. Litig. 2011 WL 2971918, at
*7-10 (N.D. Ohio July 21, 2011).
184. See DeLuca v. Merrell Dow Pharms., Inc., 911 F.2d 941, 958 (3d Cir. 1990); cf. Kehm v. Procter & Gamble Co., 580 F. Supp. 890, 902 (N.D. Iowa 1982) (“These [epidemiologic] studies were highly probative on the issue of causation—they all concluded that an association between tampon use and menstrually related TSS [toxic shock syndrome] cases exists.”), aff’d, 724 F.2d 613 (8th Cir. 1984).
Hearsay concerns may limit the independent admissibility of the study, but the study could be relied on by an expert in forming an opinion and may be admissible pursuant to Fed. R. Evid. 703 as part of the underlying facts or data relied on by the expert.
In Ellis v. International Playtex, Inc., 745 F.2d 292, 303 (4th Cir. 1984), the court concluded that certain epidemiologic studies were admissible despite criticism of the methodology used in the studies. The court held that the claims of bias went to the studies’ weight rather than their admissibility. Cf. Christophersen v. Allied-Signal Corp., 939 F.2d 1106, 1109 (5th Cir. 1991) (“As a general rule, questions relating to the bases and sources of an expert’s opinion affect the weight to be assigned that opinion rather than its admissibility. . . . “).
185. Even if evidence is relevant, it may be excluded if its probative value is substantially outweighed by prejudice, confusion, or inefficiency. Fed. R. Evid. 403. However, exclusion of an otherwise relevant epidemiologic study on Rule 403 grounds is unlikely.
In Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579, 591 (1993), the Court invoked the concept of “fit,” which addresses the relationship of an expert’s scientific opinion to the facts of the case and the issues in dispute. In a toxic substance case in which cause in fact is disputed, an epi-
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Far more courts have confronted the role that epidemiology plays with regard to the sufficiency of the evidence and the burden of production.186 The civil burden of proof is described most often as requiring belief by the factfinder “that what is sought to be proved is more likely true than not true.”187 The rela- tive risk from epidemiologic studies can be adapted to this 50%-plus standard to yield a probability or likelihood that an agent caused an individual’s disease.188 An important caveat is necessary, however. The discussion below speaks in terms of the magnitude of the relative risk or association found in a study. However, before an association or relative risk is used to make a statement about the probability of individual causation, the inferential judgment, described in Section V, that the association is truly causal rather than spurious, is required: “[A]n agent cannot be considered to cause the illness of a specific person unless it is recognized as a cause of that disease in general.”189 The following discussion should be read with this caveat in mind.190
demiologic study of the same agent to which the plaintiff was exposed that examined the association with the same disease from which the plaintiff suffers would undoubtedly have sufficient “fit” to be a part of the basis of an expert’s opinion. The Court’s concept of “fit,” borrowed from United States v. Downing, 753 F.2d 1224, 1242 (3d Cir. 1985), appears equivalent to the more familiar evidentiary concept of probative value, albeit one requiring assessment of the scientific reasoning the expert used in drawing inferences from methodology or data to opinion.
186. We reiterate a point made at the outset of this section: This discussion of the use of a threshold relative risk for specific causation is not epidemiology or an inquiry an epidemiologist would undertake. This is an effort by courts and commentators to adapt the legal standard of proof to the available scientific evidence. See supra text accompanying notes 175–179. While strength of association is a guideline for drawing an inference of causation from an association, see supra Section V, there is no specified threshold required.
187. Kevin F. O’Malley et al., Federal Jury Practice and Instructions § 104.01 (5th ed. 2000); see also United States v. Fatico, 458 F. Supp. 388, 403 (E.D.N.Y. 1978) (“Quantified, the preponderance standard would be 50%+ probable.”), aff’d, 603 F.2d 1053 (2d Cir. 1979).
188. An adherent of the frequentist school of statistics would resist this adaptation, which may explain why many epidemiologists and toxicologists also resist it. To take the step identified in the text of using an epidemiologic study outcome to determine the probability of specific causation requires a shift from a frequentist approach, which involves sampling or frequency data from an empirical test, to a subjective probability about a discrete event. Thus, a frequentist might assert, after conducting a sampling test, that 60% of the balls in an opaque container are blue. The same frequentist would resist the statement, “The probability that a single ball removed from the box and hidden behind a screen is blue is 60%.” The ball is either blue or not, and no frequentist data would permit the latter statement. “[T]here is no logically rigorous definition of what a statement of probability means with reference to an individual instance. . . .” Lee Loevinger, On Logic and Sociology, 32 Jurimetrics J. 527,
530 (1992); see also Steve Gold, Causation in Toxic Torts: Burdens of Proof, Standards of Persuasion and Statistical Evidence, 96 Yale L.J. 376, 382–92 (1986). Subjective probabilities about unique events are employed by those using Bayesian methodology. See Kaye, supra note 80, at 54–62; David H. Kaye & David A. Freedman, Reference Guide on Statistics, Section IV.D, in this manual.
189. Cole, supra note 65, at 10,284.
190. We emphasize this caveat, both because it is not intuitive and because some courts have failed to appreciate the difference between an association and a causal relationship. See, e.g., Forsyth v. Eli Lilly
& Co., Civ. No. 95-00185 ACK, 1998 U.S. Dist. LEXIS 541, at *26–*31 (D. Haw. Jan. 5, 1998). But see
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Some courts have reasoned that when epidemiologic studies find that expo- sure to the agent causes an incidence in the exposed group that is more than twice the incidence in the unexposed group (i.e., a relative risk greater than 2.0), the probability that exposure to the agent caused a similarly situated individual’s disease is greater than 50%.191 These courts, accordingly, hold that when there is group-based evidence finding that exposure to an agent causes an incidence of dis- ease in the exposed group that is more than twice the incidence in the unexposed group, the evidence is sufficient to satisfy the plaintiff’s burden of production and permit submission of specific causation to a jury. In such a case, the factfinder may find that it is more likely than not that the substance caused the particular plain- tiff’s disease. Courts, thus, have permitted expert witnesses to testify to specific causation based on the logic of the effect of a doubling of the risk.192
While this reasoning has a certain logic as far as it goes, there are a number of significant assumptions and important caveats that require explication:
1. A valid study and risk estimate. The propriety of this “doubling” reasoning depends on group studies identifying a genuine causal relationship and a reasonably reliable measure of the increased risk.193 This requires attention
Berry v. CSX Transp., Inc., 709 So. 2d 552, 568 (Fla. Dist. Ct. App. 1998) (“From epidemiologic studies demonstrating an association, an epidemiologist may or may not infer that a causal relationship exists.”).
191. An alternative, yet similar, means to address probabilities in individual cases is use of the attributable fraction parameter, also known as the attributable risk. See supra Section III.C. The attrib- utable fraction is that portion of the excess risk that can be attributed to an agent, above and beyond the background risk that is due to other causes. Thus, when the relative risk is greater than 2.0, the attributable fraction exceeds 50%.
192. For a comprehensive list of cases that support proof of causation based on group studies, see Restatement (Third) of Torts: Liability for Physical and Emotional Harm § 28 cmt. c(4) rptrs. note (2010). The Restatement catalogues those courts that require a relative risk in excess of 2.0 as a threshold for sufficient proof of specific causation and those courts that recognize that a lower relative risk than 2.0 can support specific causation, as explained below. Despite considerable disagreement on whether a relative risk of 2.0 is required or merely a taking-off point for determining the sufficiency of the evidence on specific causation, two commentators who surveyed the cases observed that “[t] here were no clear differences in outcomes as between federal and state courts.” Russellyn S. Carruth
& Bernard D. Goldstein, Relative Risk Greater than Two in Proof of Causation in Toxic Tort Litigation, 41
Jurimetrics J. 195, 199 (2001).
193. Indeed, one commentator contends that, because epidemiology is sufficiently imprecise to accurately measure small increases in risk, in general, studies that find a relative risk less than 2.0 should not be sufficient for causation. The concern is not with specific causation but with general causation and the likelihood that an association less than 2.0 is noise rather than reflecting a true causal relationship. See Michael D. Green, The Future of Proportional Liability, in Exploring Tort Law (Stuart Madden ed., 2005); see also Samuel M. Lesko & Allen A. Mitchell, The Use of Randomized Controlled Trials for Pharmacoepidemiology Studies, in Pharmacoepidemiology 599, 601 (Brian L. Strom ed., 4th ed. 2005) (“it is advisable to use extreme caution in making causal inferences from small relative risks derived from observational studies”); Gary Taubes, Epidemiology Faces Its Limits, 269 Science 164 (1995) (explaining views of several epidemiologists about a threshold relative risk of 3.0 to seriously consider a causal relationship); N.E. Breslow & N.E. Day, Statistical Methods in Cancer Research, in The Analysis

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cases).
195 “The basic premise of probability of causation is that individual risk can be determined from epidemiologic data for a representative population; however the premise only holds if the individual is truly representative of the reference population.” Council on Scientific Affairs, American Medical Association, Radioepidemiological Tables 257 JAMA 806 (1987).
198. The comment of two prominent epidemiologists on this subject is illuminating:
We cannot measure the individual risk, and assigning the average value to everyone in the category reflects nothing more than our ignorance about the determinants of lung cancer that interact with cigarette smoke. It is apparent from epidemiological data that some people can engage in chain smok- ing for many decades without developing lung cancer. Others are or will become primed by unknown circumstances and need only to add cigarette smoke to the nearly sufficient constellation of causes to initiate lung cancer. In our ignorance of these hidden causal components, the best we can do in assessing risk is to classify people according to measured causal risk indicators and then assign the average observed within a class to persons within the class.
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