Reference Manual on Scientific Evidence Third Edition (2011) / Chapter Skim
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Reference Guide on Epidemiology--Michael D. Green, D. Michal Freedman, and Leon Gordis
Reference Guide on Epidemiology--Michael D. Green, D. Michal Freedman, and Leon Gordis
Pages 549-632
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From page 549... ...
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.
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606 V II. What Role Does Epidemiology Play in Proving Specific Causation?
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The purpose of epidemiology is to better understand disease causation and to prevent disease in groups of individuals. Epidemiology assumes that disease is not distributed randomly in a group of individuals and that identifiable subgroups, including those exposed to certain agents, are at increased risk of contracting particular diseases.1 Judges and juries are regularly presented with epidemiologic evidence as the basis of an expert's opinion on causation.2 In the courtroom, epidemiologic research findings are offered to establish or dispute whether exposure to an agent3 1. Although epidemiologists may conduct studies of beneficial agents that prevent or cure disease or other medical conditions, this reference guide refers exclusively to outcomes as diseases, because they are the relevant outcomes in most judicial proceedings in which epidemiology is involved.
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Doll and Hill's studies showed that smokers who smoked 10 to 20 cigarettes a day had a lung cancer mortality rate that was about 10 times higher than that for nonsmokers.6 These studies identified an association between smoking cigarettes and death from lung cancer that contributed to the determination that smoking causes lung cancer. However, it should be emphasized that an association is not equivalent to causation.7 An association identified in an epidemiologic study may or may not be 4. E.g., Bonner v.
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Nevertheless, a substantial body of legal precedent has developed that addresses the use of epidemiologic evidence to prove causation for an individual litigant through probabilistic means, and the law developed in these cases is discussed later in this reference guide.10 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 several decades, courts frequently have confronted the use of epidemiologic studies as evidence and have recognized their utility in proving causation.
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Section VI deals with methods for combining the results of multiple epidemiologic studies and the difficulties entailed in extracting a single global measure of risk from multiple studies. Additional legal questions that arise in most toxic substances cases are whether population-based epidemiologic evidence can be used to infer specific causation, and, if so, how.
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Researchers conducting clinical trials attempt to use study designs that are placebo controlled, which means that the group not receiving the active agent or treatment is given an inactive ingredient that appears similar to the active agent under study. They also use double blinding where possible, which means that neither the participants nor those conducting the study know which group is receiving the agent or treatment and which group is given the placebo.
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In contrast to clinical studies in which potential risk factors can be controlled, epidemiologic investigations generally focus on individuals living in the community, for whom characteristics other than the one of interest, such as diet, exercise, exposure to other environmental agents, and genetic background, may distort a study's results. Because these characteristics cannot be controlled directly by the investigator, the investigator addresses their possible role in the relationship being studied by considering them in the design of the study and in the analysis and interpretation of the study results (see infra Section IV)
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In a retrospective study, the researcher will determine the proportion of individuals in the exposed group who developed the disease from available records or evidence and compare that proportion with the proportion of another group that was not exposed.25 Thus, as illustrated in Table 1, 22. For thumbnail sketches on all types of epidemiologic study designs, see Brian L Strom, Study Designs Available for Pharmacoepidemiology Studies, in Pharmacoepidemiology 17, 21–26 (Brian L
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. If the exposure causes the disease, the researcher would expect a greater proportion of the exposed individuals to develop the disease than the unexposed individuals.26 One advantage of the cohort study design is that the temporal relationship between exposure and disease can often be established more readily than in other study designs, especially a case-control design, discussed below.
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This may be because they work in that industry, or, among other reasons, because low-wage groups are exposed to other harmful agents, such as environmental toxins present in higher concentrations in their neighborhoods. In the study design, the researcher must attempt to identify factors other than the exposure that may be responsible for the increased risk of disease.
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3. Cross-sectional studies A third type of observational study is a cross-sectional study.
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Ecological studies Up to now, we have discussed studies in which data on both exposure and health outcome are obtained for each individual included in the study.33 In contrast, studies that collect data only about the group as a whole are called ecological studies.34 In ecological studies, information about individuals is generally not g athered; instead, overall rates of disease or death for different groups are obtained and compared. The objective is to identify some difference between the two groups, such as diet, genetic makeup, or alcohol consumption, that might explain differences in the risk of disease observed in the two groups.35 Such studies may be useful for identifying associations, but they rarely provide definitive causal answers.36 The difficulty is illustrated below with an ecological study of the rela tionship between dietary fat and cancer.
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Merrell Dow Pharmaceuticals, Inc., 893 F.2d 1149, 1152–53 (10th Cir.
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, Interpretation of Negative Epidemiologic Evidence for Carcinogenicity (N.J. Wald & Richard Doll eds., 1985)
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Unfortunately, IARC has conducted evaluations of only a fraction of potentially carcinogenic agents, and many suspected toxic agents cause effects other than cancer. 47. Thus, in a series of cases involving Parlodel, a lactation suppressant for mothers of newborns, efforts to conduct an epidemiologic study of its effect on causing strokes were stymied by the infrequency of such strokes in women of child-bearing age.
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Ohio July 21, 2011) (holding that animal toxicology in conjunction with other non-epidemiologic evidence can be sufficient to prove causation)
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51. 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.
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One Hour Martinizing Dry Cleaning, 180 F
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58. A relative risk cannot be calculated for a case-control study, because a case-control study begins by examining a group of persons who already have the disease. That aspect of the study design prevents a researcher from determining the rate at which individuals develop the disease.
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The reason why the odds ratio approximates the relative risk when the incidence of disease is small can be demonstrated by referring to Table 2. The odds ratio, as stated in the text, is ad/bc.
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1992) (illustrating that a relative risk of 1.55 conforms to an attributable risk of 35%, that is, (1.55 − 1.0)
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Indirect adjustment is used when the age-specific rates for a study population are not known. In that case, the overall disease/death rate for the standard/ reference population is recalculated based on the age distribution of the population of interest using the age-specific rates of the standard population.
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Incorrect study results occur in a variety of ways. A study may find a positive association (relative risk greater than 1.0)
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A study that is statistically significant has results that are unlikely to be the result of random error, although any criterion for "significance" is somewhat arbitrary. A confidence interval provides both the relative risk (or other risk measure)
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Formal procedures for statistical testing begin with the null hypothesis, which posits that there is no true association (i.e., a relative risk of 1.0) between the agent and disease under study.
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is 1.0, an epidemiologic study of a particular study population may find a relative risk greater than (or less 73. See DeLuca v. Merrell Dow Pharms., Inc., 911 F.2d 941, 945 (3d Cir.
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Thus, a p-value of .1 means that there is a 10% chance that values at least as large as the observed relative risk could have occurred by random error, with no association actually present in the population.79 To minimize false positives, epidemiologists use a convention that the p-value must fall below some selected level known as alpha or significance level for the results of the study to be statistically significant.80 Thus, an outcome is statistically significant when the observed p-value for the study falls below the preselected 76. See Magistrini v. One Hour Martinizing Dry Cleaning, 180 F
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When researchers examine many possible associations that might exist in their data -- known as data dredging -- we should expect that even if there are no true causal relationships, those researchers will find statistically significant associations in 1 of every 20 associations examined. See Rachel Nowak, Problems in Clinical Trials Go Far Beyond Misconduct, 264 Sci.
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and their use as an alternative to statistical significance in DeLuca v. Merrell Dow Pharmaceuticals, Inc., 911 F.2d 941, 948–49 (3d Cir.
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Epidemiologists have become increasingly sophisticated in addressing the issue of random error and examining the data from a study to ascertain what information they may provide about the relationship between an agent and a disease, without the necessity of rejecting all studies that are not statistically significant.86 Meta-analysis, as well, a method for pooling the results of multiple studies, sometimes can ameliorate concerns about random error.87 Calculation of a confidence interval permits a more refined assessment of appropriate inferences about the association found in an epidemiologic study.88 2d 1071, 1103 (D.
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A confidence interval shows the boundaries of the relative risk based on selected levels of alpha or statistical significance. Just as the p-value does not provide the probability that the risk estimate found in a study is correct, the confidence interval does not provide the range within which the true risk must lie.
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. For a given risk estimate, a narrower confidence interval reflects a decreased likelihood that the association found in the study would occur by chance if the true association is 1.0.89 For the example in Figure 4, the boundaries of the confidence interval with alpha set at .05 encompass a relative risk of 1.0, and the result would be said to be not statistically significant at the .05 level.
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97. We use a relative risk of 2.0 for illustrative purposes because of the legal significance courts have attributed to this magnitude of association. See infra Section VII.
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Kaye & David A Freedman, Reference Guide on Statistics, Section IV.C.1, in this manual.
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Just as cases and controls in case-control studies should be selected independently of their exposure status, so the exposed and unexposed participants in cohort studies should be selected independently of their disease risk.103 For example, if women with hysterectomies are overrepresented among exposed women in a cohort study of cervical cancer, this could overstate the association between the exposure and the disease. A further source of selection bias occurs when those selected to participate decline to participate or drop out before the study is completed.
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Brannigan et al., Risk, Statistical Inference, and the Law of Evidence: The Use of Epidemiological Data in Toxic Tort Cases, 12 Risk Analysis 343, 344–45 (1992)
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108. Two researchers who used a case-control study to examine the association between congenital heart disease and the mother's use of drugs during pregnancy corroborated interview data with the mother's medical records. See Sally Zierler & Kenneth J
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. 112. Frequently, occupational epidemiologists employ study designs that consider all agents to which those who work in a particular occupation are exposed because they are trying to determine the hazards associated with that occupation.
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113. See also Bernard D Goldstein & Mary Sue Henifin, Reference Guide on Toxicology, Section I.D, in this manual.
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2000) , in the face of a substantial body of exonerative epidemiologic evidence, the female plaintiff alleged she suffered from an atypical systemic joint disease.
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3. Other conceptual problems There are dozens of other potential biases that can occur in observational studies, which is an important reason why clinical studies (when ethical)
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1991) (discussing the possibility that confounders may lead to an erroneous inference of a causal relationship)
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and the unexposed is compared. At the conclusion of the study, the relative risk of emphysema in the drinking group is found to be 2.0, an association that suggests a possible effect)
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Often the mere possibility of uncontrolled confounding is used to call into question the results of a study. This was certainly the strategy of some seeking, or unwittingly helping, to undermine the implications of the studies persuasively linking cigarette smoking to lung cancer.
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bRR = relative risk. The relative risk for each of the cohorts is determined based on reference to the risk among nondrinkers; that is, the incidence of disease among drinkers is compared with nondrinkers for each of the three cohorts separately.
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When a study population is not representative of the general or target population, existing scientific knowledge may permit reasonable inferences about the study's broader applicability, or additional confirmatory studies of other populations may be necessary.
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Figure 5 shows actual age-adjusted lung cancer mortality rates per 100,000 person-years by urban or rural classification and smoking category.134 Figure 5: ge-adjusted lung cancer mortality rates per 100,000 person-years by A urban or rural classification and smoking category. Age-Adjusted Lung Cancer Death Rates/100,000 Person-Years Source: Adapted from E
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By examining each stratum separately, we, in effect, hold urbanization constant, and still find much higher lung cancer mortality in smokers than in nonsmokers. For each degree of urbanization, lung cancer mortality rates and smokers are shown by the dark-colored bars, and nonsmoker mortality rates are indicated by light-colored bars.
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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.
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Merrell Dow Pharmaceuticals, Inc., 745 F
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Drawing causal inferences after finding an association and considering these factors requires judgment 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.
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Although temporal relationship is often listed as one of many factors in assessing whether an inference of causation is justified, this aspect of a temporal relationship is a necessary factor: Without exposure before the disease, causation cannot exist.150 With regard to specific causation, a subject dealt with in detail in Section VII, infra, there may be circumstances in which a temporal relationship supports the existence of a causal relationship. If the latency period between exposure and outcome is known,151 then exposure consistent with that information may lend credence to a causal relationship.
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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.
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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.
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; see also Bernard D Goldstein & Mary Sue Henifin, Reference Guide on Toxicology, Section III.E, in this manual.
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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.
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Cigarette manufacturers have long claimed that because cigarettes have been linked to lung cancer, emphysema, bladder cancer, heart disease, pancreatic cancer, and other conditions, there is no specificity and the relationships are not causal. There is, however, at least one good reason why inferences about the health consequences of tobacco do not require specificity: Because tobacco and cigarette smoke are not in fact single agents but consist of numerous harmful agents, smoking represents exposure to multiple agents, with multiple possible effects.
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However, often one is confronted with nonrandomized observational studies of the effects of possible toxic substances or agents. A method for summarizing such studies is greatly needed, but when meta-analysis is applied to observational studies -- either case-control or cohort -- it becomes more controversial.174 The reason for this is that often methodological differences among studies are much more pronounced than they are in randomized trials.
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Such differences can arise from variations in study quality, or in study populations or in study designs. Such differences in results make it harder to trust a single estimate of effect; the reasons for such differences need at least to be acknowledged and, if possible, explained.176 People often tend to have an inordinate belief in the validity of the findings when a single number is attached to them, and many of the difficulties that may arise in conducting a meta-analysis, especially of observational studies such as epidemiologic ones, may consequently be overlooked.177 VII.
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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 demiology focuses on the question of general causation (i.e., is the agent capable of causing disease?
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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")
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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.
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2005) ("it is advisable to use extreme caution in making causal inferences from small relative risks derived from observational studies")
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2002) ; see generally Joseph Sanders & Julie Machal-Fulks, The Admissibility of Differential Diagnosis Testimony to Prove Causation in Toxic Tort Cases: The Interplay of Adjective and Substantive Law, 64 Law & Contemp.
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The rela tive risk of smoking in that study would not be applicable to an asbestos insulation worker. More generally, if the study subjects are heterogeneous with regard to risk factors related to the outcome of interest, the relative risk found in a study represents an average risk for the group rather than a uniform increased risk applicable to each individual.198 3.
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Employing a risk estimate to determine the probability of causation is not valid if the agent interacts with another cause in a way that results in an increase in disease beyond merely the sum of the increased incidence due to each agent separately. For example, the relative risk of lung cancer due to smoking is around 10, while the relative risk for asbestos exposure is approximately 5.
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factors is required.206 Having additional evidence that bears on individual causation has led a few courts to conclude that a plaintiff may satisfy his or her burden of production even if a relative risk less than 2.0 emerges from the epidemiologic evidence.207 For example, genetics might be known to be responsible for 50% of the incidence of a disease independent of exposure to the agent.208 If genetics can be ruled out 204. See supra Section V.C; see also Ferebee v. Chevron Chem.
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. 209. The use of probabilities in excess of .50 to support a verdict results in an all-or-nothing approach to damages that some commentators have criticized.The criticism reflects the fact that defendants responsible for toxic agents with a relative risk just above 2.0 may be required to pay damages not only for the disease that their agents caused, but also for all instances of the disease.
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; Magistrini v. One Hour Martinizing Dry Cleaning, 180 F
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alpha. The level of statistical significance chosen by a researcher to determine if any association found in a study is sufficiently unlikely to have occurred by chance (as a result of random sampling error)
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. This term has been used to denote the fraction of risk that is attributable to exposure to a substance (e.g., X percent of lung cancer is attributable to cigarettes)
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Unlike observational studies, clinical trials can be conducted as experiments and use randomization, because the agent being studied is thought to be beneficial. cohort.
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A technique used to eliminate any difference between two study populations based on age, sex, or some other parameter that might result in confounding. Direct adjustment entails comparison of the study group with a large reference population to determine the expected rates based on the characteristic, such as age, for which adjustment is being performed.
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Random error (sampling error) is the error that is due to chance when the result obtained for a sample differs from the result that would be obtained if the entire population (universe)
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A technique used to combine the results of several studies to enhance the precision of the estimate of the effect size and reduce the plausibility that the association found is due to random sampling error. Meta-nalysis is best suited to pooling results from randomly controlled a e xperimental studies, but if carefully performed, it also may be useful for observational studies.
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observational study. An epidemiologic study in situations in which nature is allowed to take its course, without intervention from the investigator.
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Other significance levels can be adopted, such as .01 or .1. The lower the p-value, the less likely that random error would have produced the observed relative risk if the true relative risk is 1.
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significance level. A somewhat arbitrary level selected to minimize the risk that an erroneous positive study outcome that is due to random error will be accepted as a true association.
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Some epidemiologists believe that formal significance testing is inferior to using a confidence interval to express the results of a study. Statistical significance, which addresses the role of random sampling error in producing the results found in the study, should not be confused with the importance (for public health or public policy)
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In a study of the causes of heart disease, blood pressure and dietary fat intake are variables that might be measured.
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. Bert Black & David Lilienfeld, Epidemiologic Proof in Toxic Tort Litigation, 52 Ford ham L
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. Troyen Brennan, Helping Courts with Toxic Torts: Some Proposals Regarding Alter native Methods for Presenting and Assessing Scientific Evidence in Common Law Courts, 51 U
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Merrell Dow Pharmaceuticals, Inc.: Statistical Significance and the Novel Scientific Technique, 58 Brook.
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