Reference Guide on Epidemiology


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 Epidemiology 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.


   I. Introduction

  II. What Different Kinds of Epidemiologic Studies Exist?

A. Experimental and Observational Studies of Suspected Toxic Agents

B. Types of Observational Study Design

1. Cohort studies

2. Case-control studies

3. Cross-sectional studies

4. Ecological studies

C. Epidemiologic and Toxicologic Studies

 III. How Should Results of an Epidemiologic Study Be Interpreted?

A. Relative Risk

B. Odds Ratio

C. Attributable Risk

D. Adjustment for Study Groups That Are Not Comparable

 IV. What Sources of Error Might Have Produced a False Result?

A. What Statistical Methods Exist to Evaluate the Possibility of Sampling Error?

1. False positives and statistical significance

2. False negatives

3. Power

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Reference Guide on Epidemiology m iChael d. Green, d. miChal freedman, and l eon 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 onTenTs 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 D. Adjustment for Study Groups That Are Not Comparable, 571 IV. What Sources of Error Might Have Produced a False Result? 572 A. What Statistical Methods Exist to Evaluate the Possibility of Sampling Error? 574 1. False positives and statistical significance, 575 2. False negatives, 581 3. Power, 582 549

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Reference Manual on Scientific Evidence B. What Biases May Have Contributed to an Erroneous Association? 583 1. Selection bias, 583 2. Information bias, 585 3. Other conceptual problems, 590 C. Could a Confounding Factor Be Responsible for the Study Result? 591 1. What techniques can be used to prevent or limit confounding? 595 2. What techniques can be used to identify confounding factors? 595 3. What techniques can be used to control for confounding factors? 596 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 VIII. Acknowledgments, 618 Glossary of Terms, 619 References on Epidemiology, 630 References on Law and Epidemiology, 630 550

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Reference Guide on Epidemiology I. Introduction Epidemiology is the field of public health and medicine that studies the incidence, distribution, and etiology of disease in human populations. The purpose of epide- miology 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. 2. Epidemiologic studies have been well received by courts deciding cases involving toxic substances. See, e.g., Siharath v. Sandoz Pharms. Corp., 131 F. Supp. 2d 1347, 1356 (N.D. Ga. 2001) (“The existence of relevant epidemiologic studies can be a significant factor in proving general causa- tion in toxic tort cases. Indeed, epidemiologic studies provide ‘the primary generally accepted meth- odology for demonstrating a causal relation between a chemical compound and a set of symptoms or disease.’” (quoting Conde v. Velsicol Chem. Corp., 804 F. Supp. 972, 1025–26 (S.D. Ohio 1992))), aff’d, 295 F.3d 1194 (11th Cir. 2002); Berry v. CSX Transp., Inc., 709 So. 2d 552, 569 (Fla. Dist. Ct. App. 1998). Well-conducted studies are uniformly admitted. 3 Modern Scientific Evidence: The Law and Science of Expert Testimony § 23.1, at 187 (David L. Faigman et al. eds., 2007–08) [hereinafter Modern Scientific Evidence]. Since Daubert v. Merrell Dow Pharmaceuticals, 509 U.S. 579 (1993), the predominant use of epidemiologic studies is in connection with motions to exclude the testimony of expert witnesses. Cases deciding such motions routinely address epidemiology and its implications for the admissibility of expert testimony on causation. Often it is not the investigator who conducted the study who is serving as an expert witness in a case in which the study bears on causation. See, e.g., Kennedy v. Collagen Corp., 161 F.3d 1226 (9th Cir. 1998) (physician is permitted to testify about causation); DeLuca v. Merrell Dow Pharms., Inc., 911 F.2d 941, 953 (3d Cir. 1990) (a pediatric phar- macologist expert’s credentials are sufficient pursuant to Fed. R. Evid. 702 to interpret epidemiologic studies and render an opinion based thereon); Medalen v. Tiger Drylac U.S.A., Inc., 269 F. Supp. 2d 1118, 1129 (D. Minn. 2003) (holding toxicologist could testify to general causation but not specific causation); Burton v. R.J. Reynolds Tobacco Co., 181 F. Supp. 2d 1256, 1267 (D. Kan. 2002) (a vascular surgeon was permitted to testify to general causation); Landrigan v. Celotex Corp., 605 A.2d 1079, 1088 (N.J. 1992) (an epidemiologist was permitted to testify to both general causation and spe- cific causation); Trach v. Fellin, 817 A.2d 1102, 1117–18 (Pa. Super. Ct. 2003) (an expert who was a toxicologist and pathologist was permitted to testify to general and specific causation). 3. We use the term “agent” to refer to any substance external to the human body that potentially causes disease or other health effects. Thus, drugs, devices, chemicals, radiation, and minerals (e.g., asbestos) are all agents whose toxicity an epidemiologist might explore. A single agent or a number of independent agents may cause disease, or the combined presence of two or more agents may be necessary for the development of the disease. Epidemiologists also conduct studies of individual charac- teristics, such as blood pressure and diet, which might pose risks, but those studies are rarely of interest in judicial proceedings. Epidemiologists also may conduct studies of drugs and other pharmaceutical products to assess their efficacy and safety. 551

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Reference Manual on Scientific Evidence caused a harmful effect or disease.4 Epidemiologic evidence identifies agents that are associated with an increased risk of disease in groups of individuals, quantifies the amount of excess disease that is associated with an agent, and provides a profile of the type of individual who is likely to contract a disease after being exposed to an agent. Epidemiology focuses on the question of general causation (i.e., is the agent capable of causing disease?) rather than that of specific causation (i.e., did it cause disease in a particular individual?).5 For example, in the 1950s, Doll and Hill and others published articles about the increased risk of lung cancer in cigarette smokers. 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 smok- ing 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 cau- sation.7 An association identified in an epidemiologic study may or may not be 4. E.g., Bonner v. ISP Techs., Inc., 259 F.3d 924 (8th Cir. 2001) (a worker exposed to organic solvents allegedly suffered organic brain dysfunction); Burton v. R.J. Reynolds Tobacco Co., 181 F. Supp. 2d 1256 (D. Kan. 2002) (cigarette smoking was alleged to have caused peripheral vascular disease); In re Bextra & Celebrex Mktg. Sales Practices & Prod. Liab. Litig., 524 F. Supp. 2d 1166 (N.D. Cal. 2007) (multidistrict litigation over drugs for arthritic pain that caused heart disease); Ruff v. Ensign-Bickford Indus., Inc., 168 F. Supp. 2d 1271 (D. Utah 2001) (chemicals that escaped from an explosives manufacturing site allegedly caused non-Hodgkin’s lymphoma in nearby residents); Castillo v. E.I. du Pont De Nemours & Co., 854 So. 2d 1264 (Fla. 2003) (a child born with a birth defect allegedly resulting from mother’s exposure to a fungicide). 5. This terminology and the distinction between general causation and specific causation are widely recognized in court opinions. See, e.g., Norris v. Baxter Healthcare Corp., 397 F.3d 878 (10th Cir. 2005); In re Hanford Nuclear Reservation Litig., 292 F.3d 1124, 1129 (9th Cir. 2002) (“‘Generic causation’ has typically been understood to mean the capacity of a toxic agent . . . to cause the illnesses complained of by plaintiffs. If such capacity is established, ‘individual causation’ answers whether that toxic agent actually caused a particular plaintiff’s illness.”); In re Rezulin Prods. Liab. Litig., 369 F. Supp. 2d 398, 402 (S.D.N.Y. 2005); Soldo v. Sandoz Pharms. Corp., 244 F. Supp. 2d 434, 524–25 (W.D. Pa. 2003); Burton v. R.J. Reynolds Tobacco Co., 181 F. Supp. 2d 1256, 1266–67 (D. Kan. 2002). For a discussion of specific causation, see infra Section VII. 6. Richard Doll & A. Bradford Hill, Lung Cancer and Other Causes of Death in Relation to Smoking: A Second Report on the Mortality of British Doctors, 2 Brit. Med. J. 1071 (1956). 7. See Soldo v. Sandoz Pharms. Corp., 244 F. Supp. 2d 434, 461 (W.D. Pa. 2003) (Hill criteria [see infra Section V] developed to assess whether an association is causal); Miller v. Pfizer, Inc., 196 F. Supp. 2d 1062, 1079–80 (D. Kan. 2002); Magistrini v. One Hour Martinizing Dry Cleaning, 180 F. Supp. 2d 584, 591 (D.N.J. 2002) (“[A]n association is not equivalent to causation.” (quoting the second edition of this reference guide)); Zandi v. Wyeth a/k/a Wyeth, Inc., No. 27-CV-06-6744, 2007 WL 3224242, at *11 (D. Minn. Oct. 15, 2007). Association is more fully discussed infra Section III. The term is used to describe the relationship between two events (e.g., exposure to a chemical agent and development of disease) that occur more frequently together than one would expect by chance. Association does not necessarily imply a causal effect. Causation is used to describe the association between two events when one event is a necessary link in a chain of events that results in the effect. Of course, alternative causal chains may exist that do not include the agent but that result in the same effect. For general treatment of causation in tort law 552

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Reference Guide on Epidemiology causal.8 Assessing whether an association is causal requires an understanding of the strengths and weaknesses of the study’s design and implementation, as well as a judgment about how the study findings fit with other scientific knowledge. It is important to emphasize that all studies have “flaws” in the sense of limitations that add uncertainty about the proper interpretation of the results.9 Some flaws are inevitable given the limits of technology, resources, the ability and willingness of persons to participate in a study, and ethical constraints. In evaluating epidemio- logic evidence, the key questions, then, are the extent to which a study’s limita- tions compromise its findings and permit inferences about causation. A final caveat is that employing the results of group-based studies of risk to make a causal determination for an individual plaintiff is beyond the limits of epidemiology. Nevertheless, a substantial body of legal precedent has developed that addresses the use of epidemiologic evidence to prove causation for an indi- vidual 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 fre- quently have confronted the use of epidemiologic studies as evidence and have recognized 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 epidemiologic data is generally a fit subject for judi- cial notice; epidemiology is a well-established branch of science and medicine, and epidemiologic evidence has been accepted in numerous cases.”11 Indeed, and that for factual causation to exist an agent must be a necessary link in a causal chain sufficient for the outcome, see Restatement (Third) of Torts: Liability for Physical Harm § 26 (2010). Epidemiologic methods cannot deductively prove causation; indeed, all empirically based science cannot affirmatively prove a causal relation. See, e.g., Stephan F. Lanes, The Logic of Causal Inference in Medicine, in Causal Inference 59 (Kenneth J. Rothman ed., 1988). However, epidemiologic evidence can justify an infer- ence that an agent causes a disease. See infra Section V. 8. See infra Section IV. 9. See In re Phenylpropanolamine (PPA) Prods. Liab. Litig., 289 F. Supp. 2d 1230, 1240 (W.D. Wash. 2003) (quoting this reference guide and criticizing defendant’s “ex post facto dissection” of a study); In re Orthopedic Bone Screw Prods. Liab. Litig., MDL No. 1014, 1997 U.S. Dist. LEXIS 6441, at *26–*27 (E.D. Pa. May 5, 1997) (holding that despite potential for several biases in a study that “may . . . render its conclusions inaccurate,” the study was sufficiently reliable to be admissible); Joseph L. Gastwirth, Reference Guide on Survey Research, 36 Jurimetrics J. 181, 185 (1996) (review essay) (“One can always point to a potential flaw in a statistical analysis.”). 10. See infra Section VII. 11. 911 F.2d 941, 954 (3d Cir. 1990); see also Norris v. Baxter Healthcare Corp., 397 F.3d 878, 882 (10th Cir. 2005) (an extensive body of exonerative epidemiologic evidence must be confronted and the plaintiff must provide scientifically reliable contrary evidence); In re Meridia Prods. Liab. Litig., 328 F. Supp. 2d 791, 800 (N.D. Ohio 2004) (“Epidemiologic studies are the primary gener- ally accepted methodology for demonstrating a causal relation between the chemical compound and a set of symptoms or a disease. . . .” (quoting Conde v. Velsicol Chem. Corp., 804 F. Supp. 972, 553

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Reference Manual on Scientific Evidence much more difficult problems arise for courts when there is a paucity of epide- miologic evidence.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 or studies reveal an association between an agent and disease? 2. Could this association have resulted from limitations of the study (bias, confounding, or sampling error), and, if so, from which? 3. Based on the analysis of limitations in Item 2, above, and on other evi- dence, how plausible is a causal interpretation of the association? Section II explains the different kinds of epidemiologic studies, and Section III addresses the meaning of their outcomes. Section IV examines concerns about the methodological validity of a study, including the problem of sampling error.13 Section V discusses general causation, considering whether an agent is capable of causing disease. Section VI deals with methods for combining the results of mul- tiple 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. Section VII addresses specific causation—the matter of whether a specific agent caused the disease in a given plaintiff. 1025–26 (S.D. Ohio 1992))); Brasher v. Sandoz Pharms. Corp., 160 F. Supp. 2d 1291, 1296 (N.D. Ala. 2001) (“Unquestionably, epidemiologic studies provide the best proof of the general association of a particular substance with particular effects, but it is not the only scientific basis on which those effects can be predicted.”). 12. See infra note 181. 13. For a more in-depth discussion of the statistical basis of epidemiology, see David H. Kaye & David A. Freedman, Reference Guide on Statistics, Section II.A, in this manual, and two case studies: Joseph Sanders, The Bendectin Litigation: A Case Study in the Life Cycle of Mass Torts, 43 Hastings L.J. 301 (1992); Devra L. Davis et al., Assessing the Power and Quality of Epidemiologic Studies of Asbestos- Exposed Populations, 1 Toxicological & Indus. Health 93 (1985). See also References on Epidemiology and References on Law and Epidemiology at the end of this reference guide. 554

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Reference Guide on Epidemiology II. What Different Kinds of Epidemiologic Studies Exist? A. Experimental and Observational Studies of Suspected Toxic Agents To determine whether an agent is related to the risk of developing a certain disease or an adverse health outcome, we might ideally want to conduct an experimental study in which the subjects would be randomly assigned to one of two groups: one group exposed to the agent of interest and the other not exposed. After a period of time, the study participants in both groups would be evaluated for the development of the disease. This type of study, called a randomized trial, clini- cal trial, or true experiment, is considered the gold standard for determining the relationship of an agent to a health outcome or adverse side effect. Such a study design is often used to evaluate new drugs or medical treatments and is the best way to ensure that any observed difference in outcome between the two groups is likely to be the result of exposure to the drug or medical treatment. Randomization minimizes the likelihood that there are differences in rel- evant characteristics between those exposed to the agent and those not exposed. Researchers conducting clinical trials attempt to use study designs that are placebo controlled, which means that the group not receiving the active agent or treat- ment 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. However, ethical and practical constraints limit the use of such experimental methodologies to assess the value of agents that are thought to be beneficial to human beings.14 When an agent’s effects are suspected to be harmful, researchers cannot knowingly expose people to the agent.15 Instead epidemiologic studies typically 14. Although experimental human studies cannot intentionally expose subjects to toxins, they can provide evidence that a new drug or other beneficial intervention also has adverse effects. See In re Bextra & Celebrex Mktg. Sales Practices & Prod. Liab. Litig., 524 F. Supp. 2d 1166, 1181 (N.D. Cal. 2007) (the court relied on a clinical study of Celebrex that revealed increased cardiovascular risk to conclude that the plaintiff’s experts’ testimony on causation was admissible); McDarby v. Merck & Co., 949 A.2d 223 (N.J. Super. Ct. App. Div. 2008) (explaining how clinical trials of Vioxx revealed an association with heart disease). 15. Experimental studies in which human beings are exposed to agents known or thought to be toxic are ethically proscribed. See Glastetter v. Novartis Pharms. Corp., 252 F.3d 986, 992 (8th Cir. 2001); Brasher v. Sandoz Pharms. Corp., 160 F. Supp. 2d 1291, 1297 (N.D. Ala. 2001). Experimental studies can be used where the agent under investigation is believed to be beneficial, as is the case in the development and testing of new pharmaceutical drugs. See, e.g., McDarby v. Merck & Co., 949 A.2d 223, 270 (N.J. Super. Ct. App. Div. 2008) (an expert witness relied on a clinical trial of a new drug to find the adjusted risk for the plaintiff); see also Gordon H. Guyatt, Using Randomized Trials in 555

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Reference Manual on Scientific Evidence “observe”16 a group of individuals who have been exposed to an agent of interest, such as cigarette smoke or an industrial chemical and compare them with another group of individuals who have not been exposed. Thus, the investigator identifies a group of subjects who have been exposed17 and compares their rate of disease or death with that of an unexposed group. 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).18 We emphasize that the Achilles’ heel of observational studies is the possibility of differences in the two populations being studied with regard to risk factors other than exposure to the agent.19 By contrast, experimental studies, in which subjects are randomized, generally avoid this problem. B. Types of Observational Study Design Several different types of observational epidemiologic studies can be conducted.20 Study designs may be chosen because of suitability for investigating the question of interest, timing constraints, resource limitations, or other considerations. Most observational studies collect data about both exposure and health out- come in every individual in the study. The two main types of observational studies are cohort studies and case-control studies. A third type of observational study is a cross-sectional study, although cross-sectional studies are rarely useful in identify- ing toxic agents.21 A final type of observational study, one in which data about Pharmacoepidemiology, in Drug Epidemiology and Post-Marketing Surveillance 59 (Brian L. Strom & Giampaolo Velo eds., 1992). Experimental studies also may be conducted that entail the discontinu- ation of exposure to a harmful agent, such as studies in which smokers are randomly assigned to a variety of smoking cessation programs or have no cessation. 16. Classifying these studies as observational in contrast to randomized trials can be mislead- ing to those who are unfamiliar with the area, because subjects in a randomized trial are observed as well. Nevertheless, the use of the term “observational studies” to distinguish them from experimental studies is widely employed. 17. The subjects may have voluntarily exposed themselves to the agent of interest, as is the case, for example, for those who smoke cigarettes, or subjects may have been exposed involuntarily or even with- out knowledge to an agent, such as in the case of employees who are exposed to chemical fumes at work. 18. See David A. Freedman, Oasis or Mirage? 21 Chance 59, 59–61 (Mar. 2008). 19. Both experimental and observational studies are subject to random error. See infra Sec- tion IV.A. 20. Other epidemiologic studies collect data about the group as a whole, rather than about each individual in the group. These group studies are discussed infra Section II.B.4. 21. See infra Section II.B.3. 556

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Reference Guide on Epidemiology individuals are not gathered, but rather population data about exposure and disease are used, is an ecological study.22 The difference between cohort studies and case-control studies is that cohort studies measure and compare the incidence of disease in the exposed and unexposed (“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”). In a case-control study, the rates of exposure in the cases and the rates in the controls are compared, and the odds of having the disease when exposed to a suspected agent can be compared with the odds when not exposed. The critical difference between cohort studies and case- control studies is that cohort studies begin with exposed people and unexposed people, while case-control studies begin with individuals who are selected based on whether they have the disease or do not have the disease and their exposure to the agent in question is measured. The goal of both types of studies is to deter- mine if there is an association between exposure to an agent and a disease and the strength (magnitude) of that association. 1. Cohort studies In cohort studies,23 researchers define a study population without regard to the participants’ disease status. The cohort may be defined in the present and followed forward into the future (prospectively) or it may be constructed retrospectively as of sometime in the past and followed over historical time toward the present. In either case, the researchers classify the study participants into groups based on whether they were exposed to the agent of interest (see Figure 1).24 In a prospec- tive study, the exposed and unexposed groups are followed for a specified length of time, and the proportions of individuals in each group who develop the disease of interest are compared. 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 pro- portion 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. Strom ed., 4th ed. 2005). 23. Cohort studies also are referred to as prospective studies and followup studies. 24. In some studies, there may be several groups, each with a different magnitude of exposure to the agent being studied. Thus, a study of cigarette smokers might include heavy smokers (>3 packs a day), moderate smokers (1 to 2 packs a day), and light smokers (<1 pack a day). See, e.g., Robert A. Rinsky et al., Benzene and Leukemia: An Epidemiologic Risk Assessment, 316 New Eng. J. Med. 1044 (1987). 25. Sometimes in retrospective cohort studies the researcher gathers historical data about expo- sure and disease outcome of a cohort. Harold A. Kahn, An Introduction to Epidemiologic Methods 39–41 (1983). Irving Selikoff, in his seminal study of asbestotic disease in insulation workers, included several hundred workers who had died before he began the study. Selikoff was able to obtain infor- mation about exposure from union records and information about disease from hospital and autopsy 557

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Reference Manual on Scientific Evidence Figure 1. Design of a cohort study. Defined Population Exposed Not Exposed Do Not Do Not Develop Develop Develop Develop Disease Disease Disease Disease Table 1. Cross-Tabulation of Exposure by Disease Status Figure 10-1.eps Incidence Rates No Disease Disease Totals of Disease Not exposed a+c c /(a + c) a c Exposed b+d d/(b + d) b d a researcher would compare the proportion of unexposed individuals with the disease, c /(a + c), with the proportion of exposed individuals with the disease, d/(b + d). 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. By tracking people who are initially not affected by the disease, the researcher can determine the time of disease onset and its relation to exposure. This temporal relationship is criti- cal to the question of causation, because exposure must precede disease onset if exposure caused the disease. As an example, in 1950 a cohort study was begun to determine whether uranium miners exposed to radon were at increased risk for lung cancer as com- records. Irving J. Selikoff et al., The Occurrence of Asbestosis Among Insulation Workers in the United States, 132 Ann. N.Y. Acad. Sci. 139, 143 (1965). 26. Researchers often examine the rate of disease or death in the exposed and control groups. The rate of disease or death entails consideration of the number developing disease within a specified period. All smokers and nonsmokers will, if followed for 100 years, die. Smokers will die at a greater rate than nonsmokers in the earlier years. 558

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Reference Guide on Epidemiology pared with nonminers. The study group (also referred to as the exposed cohort) consisted of 3400 white, underground miners. The control group (which need not be the same size as the exposed cohort) comprised white nonminers from the same geographic area. Members of the exposed cohort were examined every 3 years, and the degree of this cohort’s exposure to radon was measured from samples taken in the mines. Ongoing testing for radioactivity and periodic medical moni- toring of lungs permitted the researchers to examine whether disease was linked to prior work exposure to radiation and allowed them to discern the relationship between exposure to radiation and disease. Exposure to radiation was associated with the development of lung cancer in uranium miners.27 The cohort design is used often in occupational studies such as the one just dis- cussed. Because the design is not experimental, and the investigator has no control over what other exposures a subject in the study may have had, an increased risk of disease among the exposed group may be caused by agents other than the exposure of interest. A cohort study of workers in a certain industry that pays below-average wages might find a higher risk of cancer in those workers. 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. If data are gathered on other possible etiologic factors, the researcher generally uses statistical methods28 to assess whether a true associa- tion exists between working in the industry and cancer. Evaluating whether the association is causal involves additional analysis, as discussed in Section V. 2. Case-control studies In case-control studies,29 the researcher begins with a group of individuals who have a disease (cases) and then selects a similar group of individuals who do not have the disease (controls). (Ideally, controls should come from the same source population as the cases.) The researcher then compares the groups in terms of past exposures. If a certain exposure is associated with or caused the disease, a higher proportion of past exposure among the cases than among the controls would be expected (see Figure 2). 27. This example is based on a study description in Abraham M. Lilienfeld & David E. Lilien- feld, Foundations of Epidemiology 237–39 (2d ed. 1980). The original study is Joseph K. Wagoner et al., Radiation as the Cause of Lung Cancer Among Uranium Miners, 273 New Eng. J. Med. 181 (1965). 28. See Daniel L. Rubinfeld, Reference Guide on Multiple Regression, Section II.B, in this manual; David H. Kaye & David A. Freedman, Reference Guide on Statistics, Section V.D, in this manual. 29. Case-control studies are also referred to as retrospective studies, because researchers gather historical information about rates of exposure to an agent in the case and control groups. 559

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Reference Manual on Scientific Evidence determine if there is a relationship between the existence of the variables and disease. Because cross-sectional studies examine only a particular moment in time, they reflect the prevalence (existence) rather than the incidence (rate) of disease and can offer only a limited view of the causal association between the variables and disease. Because exposures to toxic agents often change over time, cross-sectional studies are rarely used to assess the toxicity of exogenous agents. data dredging. Jargon that refers to results identified by researchers who, after completing a study, pore through their data seeking to find any associations that may exist. In general, good research practice is to identify the hypotheses to be investigated in advance of the study; hence, data dredging is generally frowned on. In some cases, however, researchers conduct exploratory studies designed to generate hypotheses for further study. demographic study. See ecological study. dependent variable. The outcome that is being assessed in a study based on the effect of another characteristic—the independent variable. Epidemiologic studies attempt to determine whether there is an association between the independent variable (exposure) and the dependent variable (incidence of disease). differential misclassification. A form of bias that is due to the misclassification of individuals or a variable of interest when the misclassification varies among study groups. This type of bias occurs when, for example, it is incorrectly determined that individuals in a study are unexposed to the agent being studied when in fact they are exposed. See nondifferential misclassification. direct adjustment. 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. dose. Generally refers to the intensity or magnitude of exposure to an agent multiplied by the duration of exposure. Dose may be used to refer only to the intensity of exposure. dose–response relationship. A relationship in which a change in amount, intensity, or duration of exposure to an agent is associated with a change— either an increase or a decrease—in risk of disease. double blinding. A method used in experimental studies in which neither the individuals being studied nor the researchers know during the study whether any individual has been assigned to the exposed or control group. Double blinding is designed to prevent knowledge of the group to which the indi- vidual was assigned from biasing the outcome of the study. 622

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Reference Guide on Epidemiology ecological fallacy. Also, aggregation bias, ecological bias. An error that occurs from inferring that a relationship that exists for groups is also true for indi- viduals. For example, if a country with a higher proportion of fishermen also has a higher rate of suicides, then inferring that fishermen must be more likely to commit suicide is an ecological fallacy. ecological study. Also, demographic study. A study of the occurrence of disease based on data from populations, rather than from individuals. An ecological study searches for associations between the incidence of disease and suspected disease-causing agents in the studied populations. Researchers often conduct ecological studies by examining easily available health statistics, making these studies relatively inexpensive in comparison with studies that measure disease and exposure to agents on an individual basis. epidemiology. The study of the distribution and determinants of disease or other health-related states and events in populations and the application of this study to control of health problems. error. 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) were studied. etiologic factor. An agent that plays a role in causing a disease. etiology. The cause of disease or other outcome of interest. experimental study. A study in which the researcher directly controls the condi- tions. Experimental epidemiology studies (also clinical studies) entail random assignment of participants to the exposed and control groups (or some other method of assignment designed to minimize differences between the groups). exposed, exposure. In epidemiology, the exposed group (or the exposed) is used to describe a group whose members have been exposed to an agent that may be a cause of a disease or health effect of interest, or possess a characteristic that is a determinant of a health outcome. false-negative error. See beta error. false-positive error. See alpha error. followup study. See cohort study. general causation. Issue of whether an agent increases the incidence of disease in a group and not whether the agent caused any given individual’s disease. Because of individual variation, a toxic agent generally will not cause disease in every exposed individual. generalizable. When the results of a study are applicable to populations other than the study population, such as the general population. in vitro. Within an artificial environment, such as a test tube (e.g., the cultivation of tissue in vitro). in vivo. Within a living organism (e.g., the cultivation of tissue in vivo). 623

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Reference Manual on Scientific Evidence incidence rate. The number of people in a specified population falling ill from a particular disease during a given period. More generally, the number of new events (e.g., new cases of a disease in a defined population) within a specified period of time. incidence study. See cohort study. independent variable. A characteristic that is measured in a study and that is suspected to have an effect on the outcome of interest (the dependent vari- able). Thus, exposure to an agent is measured in a cohort study to determine whether that independent variable has an effect on the incidence of disease, which is the dependent variable. indirect adjustment. A technique employed to minimize error that might result when comparing two populations because of differences in age, sex, or another parameter that may independently affect the rate of disease in the populations. The incidence of disease in a large reference population, such as all residents of a country, is calculated for each subpopulation (based on the relevant parameter, such as age). Those incidence rates are then applied to the study population with its distribution of persons to determine the overall incidence rate for the study population, which provides a standardized mor- tality or morbidity ratio (often referred to as SMR). inference. The intellectual process of making generalizations from observations. In statistics, the development of generalizations from sample data, usually with calculated degrees of uncertainty. information bias. Also, observational bias. Systematic error in measuring data that results in differential accuracy of information (such as exposure status) for comparison groups. interaction. When the magnitude or direction (positive or negative) of the effect of one risk factor differs depending on the presence or level of the other. In interaction, the effect of two risk factors together is different (greater or less) than the sum of their individual effects. meta-analysis. 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-analysis is best suited to pooling results from randomly controlled experimental studies, but if carefully performed, it also may be useful for observational studies. misclassification bias. The erroneous classification of an individual in a study as exposed to the agent when the individual was not, or incorrectly classifying a study individual with regard to disease. Misclassification bias may exist in all study groups (nondifferential misclassification) or may vary among groups (differential misclassification). 624

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Reference Guide on Epidemiology morbidity rate. State of illness or disease. Morbidity rate may refer to either the incidence rate or prevalence rate of disease. mortality rate. Proportion of a population that dies of a disease or of all causes. The numerator is the number of individuals dying; the denominator is the total population in which the deaths occurred. The unit of time is usually a calendar year. model. A representation or simulation of an actual situation. This may be either (1) a mathematical representation of characteristics of a situation that can be manipulated to examine consequences of various actions; (2) a representa- tion of a country’s situation through an “average region” with characteristics resembling those of the whole country; or (3) the use of animals as a substitute for humans in an experimental system to ascertain an outcome of interest. multivariate analysis. A set of techniques used when the variation in several variables has to be studied simultaneously. In statistics, any analytical method that allows the simultaneous study of two or more independent factors or variables. nondifferential misclassification. Error due to misclassification of individuals or a variable of interest into the wrong category when the misclassification varies among study groups. The error may result from limitations in data collection, may result in bias, and will often produce an underestimate of the true association. See differential misclassification. null hypothesis. A hypothesis that states that there is no true association between a variable and an outcome. At the outset of any observational or experimental study, the researcher must state a proposition that will be tested in the study. In epidemiology, this proposition typically addresses the existence of an association between an agent and a disease. Most often, the null hypothesis is a statement that exposure to Agent A does not increase the occurrence of Disease D. The results of the study may justify a conclusion that the null hypothesis (no association) has been disproved (e.g., a study that finds a strong association between smoking and lung cancer). A study may fail to disprove the null hypothesis, but that alone does not justify a conclusion that the null hypothesis has been proved. observational study. An epidemiologic study in situations in which nature is allowed to take its course, without intervention from the investigator. For example, in an observational study the subjects of the study are permitted to determine their level of exposure to an agent. odds ratio (OR). Also, cross-product ratio, relative odds. The ratio of the odds that a case (one with the disease) was exposed to the odds that a control (one without the disease) was exposed. For most purposes the odds ratio from a case-control study is quite similar to a risk ratio from a cohort study. 625

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Reference Manual on Scientific Evidence p (probability), p-value. The p-value is the probability of getting a value of the test outcome equal to or more extreme than the result observed, given that the null hypothesis is true. The letter p, followed by the abbreviation “n.s.” (not significant) means that p > .05 and that the association was not statistically significant at the .05 level of significance. The statement “p < .05” means that p is less than 5%, and, by convention, the result is deemed statisti- cally significant. 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. pathognomonic. When an agent must be present for a disease to occur. Thus, asbestos is a pathognomonic agent for asbestosis. See signature disease. placebo controlled. In an experimental study, providing an inert substance to the control group, so as to keep the control and exposed groups ignorant of their status. power. The probability that a difference of a specified amount will be detected by the statistical hypothesis test, given that a difference exists. In less formal terms, power is like the strength of a magnifying lens in its capability to iden- tify an association that truly exists. Power is equivalent to one minus Type II error. This is sometimes stated as Power = 1 – β. prevalence. The percentage of persons with a disease in a population at a specific point in time. prospective study. A study in which two groups of individuals are identified: (1) individuals who have been exposed to a risk factor and (2) individuals who have not been exposed. Both groups are followed for a specified length of time, and the proportion that develops disease in the first group is compared with the proportion that develops disease in the second group. See cohort study. random. The term implies that an event is governed by chance. See randomization. randomization. Assignment of individuals to groups (e.g., for experimental and control regimens) by chance. Within the limits of chance variation, random- ization should make the control group and experimental group similar at the start of an investigation and ensure that personal judgment and prejudices of the investigator do not influence assignment. Randomization should not be confused with haphazard assignment. Random assignment follows a predeter- mined plan that usually is devised with the aid of a table of random numbers. Randomization cannot ethically be used where the exposure is known to cause harm (e.g., cigarette smoking). randomized trial. See clinical trial. recall bias. Systematic error resulting from differences between two groups in a study in accuracy of memory. For example, subjects who have a disease may recall exposure to an agent more frequently than subjects who do not have the disease. 626

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Reference Guide on Epidemiology relative risk (RR). The ratio of the risk of disease or death among people exposed to an agent to the risk among the unexposed. For instance, if 10% of all people exposed to a chemical develop a disease, compared with 5% of people who are not exposed, the disease occurs twice as frequently among the exposed people. The relative risk is 10%/5% = 2. A relative risk of 1 indicates no association between exposure and disease. research design. The procedures and methods, predetermined by an investigator, to be adhered to in conducting a research project. risk. A probability that an event will occur (e.g., that an individual will become ill or die within a stated period of time or by a certain age). risk difference (RD). The difference between the proportion of disease in the exposed population and the proportion of disease in the unexposed popula- tion. –1.0 ≤ RD ≥ 1.0. sample. A selected subset of a population. A sample may be random or nonrandom. sample size. The number of subjects who participate in a study. secular-trend study. Also, time-line study. A study that examines changes over a period of time, generally years or decades. Examples include the decline of tuberculosis mortality and the rise, followed by a decline, in coronary heart disease mortality in the United States in the past 50 years. selection bias. Systematic error that results from individuals being selected for the different groups in an observational study who have differences other than the ones that are being examined in the study. sensitivity. Measure of the accuracy of a diagnostic or screening test or device in identifying disease (or some other outcome) when it truly exists. For example, assume that we know that 20 women in a group of 1000 women have cervi- cal cancer. If the entire group of 1000 women is tested for cervical cancer and the screening test only identifies 15 (of the known 20) cases of cervical cancer, the screening test has a sensitivity of 15/20, or 75%. Also see specificity. signature disease. A disease that is associated uniquely with exposure to an agent (e.g., asbestosis and exposure to asbestos). See also pathognomonic. 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. The lower the significance level selected, the less likely that false-positive error will occur. specific causation. Whether exposure to an agent was responsible for a given individual’s disease. specificity. Measure of the accuracy of a diagnostic or screening test in identify- ing those who are disease-free. Once again, assume that 980 women out of a group of 1000 women do not have cervical cancer. If the entire group of 1000 women is screened for cervical cancer and the screening test only iden- 627

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Reference Manual on Scientific Evidence tifies 900 women without cervical cancer, the screening test has a specificity of 900/980, or 92%. standardized morbidity ratio (SMR). The ratio of the incidence of disease observed in the study population to the incidence of disease that would be expected if the study population had the same incidence of disease as some selected reference population. standardized mortality ratio (SMR). The ratio of the incidence of death observed in the study population to the incidence of death that would be expected if the study population had the same incidence of death as some selected standard or known population. statistical significance. A term used to describe a study result or difference that exceeds the Type I error rate (or p-value) that was selected by the researcher at the outset of the study. In formal significance testing, a statisti- cally significant result is unlikely to be the result of random sampling error and justifies rejection of the null hypothesis. 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) of a research finding. stratification. Separating a group into subgroups based on specified criteria, such as age, gender, or socioeconomic status. Stratification is used both to control for the possibility of confounding (by separating the studied populations based on the suspected confounding factor) and when there are other known fac- tors that affect the disease under study. Thus, the incidence of death increases with age, and a study of mortality might use stratification of the cohort and control groups based on age. study design. See research design. systematic error. See bias. teratogen. An agent that produces abnormalities in the embryo or fetus by dis- turbing maternal health or by acting directly on the fetus in utero. teratogenicity. The capacity for an agent to produce abnormalities in the embryo or fetus. threshold phenomenon. A certain level of exposure to an agent below which disease does not occur and above which disease does occur. time-line study. See secular-trend study. toxicology. The science of the nature and effects of poisons. Toxicologists study adverse health effects of agents on biological organisms, such as live animals and cells. Studies of humans are performed by epidemiologists. toxic substance. A substance that is poisonous. 628

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Reference Guide on Epidemiology true association. Also, real association. The association that really exists between exposure to an agent and a disease and that might be found by a perfect (but nonetheless nonexistent) study. Type I error. Rejecting the null hypothesis when it is true. See alpha error. Type II error. Failing to reject the null hypothesis when it is false. See beta error. validity. The degree to which a measurement measures what it purports to mea- sure; the accuracy of a measurement. variable. Any attribute, condition, or other characteristic of subjects in a study that can have different numerical characteristics. In a study of the causes of heart disease, blood pressure and dietary fat intake are variables that might be measured. 629

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Reference Manual on Scientific Evidence References on Epidemiology Causal Inferences (Kenneth J. Rothman ed., 1988). William G. Cochran, Sampling Techniques (1977). A Dictionary of Epidemiology (John M. Last et al. eds., 5th ed. 2008). Anders Ahlbom & Steffan Norell, Introduction to Modern Epidemiology (2d ed. 1990). Robert C. Elston & William D. Johnson, Basic Biostatistices for Geneticists and Epidemiologists (2008) Encyclopedia of Epidemiology (Sarah E. Boslaugh ed., 2008). Joseph L. Fleiss et al., Statistical Methods for Rates and Proportions (3d ed. 2003). Leon Gordis, Epidemiology (4th ed. 2009). Morton Hunt, How Science Takes Stock: The Story of Meta-Analysis (1997). International Agency for Research on Cancer (IARC), Interpretation of Nega- tive Epidemiologic Evidence for Carcinogenicity (N.J. Wald & R. Doll eds., 1985). Harold A. Kahn & Christopher T. Sempos, Statistical Methods in Epidemiology (1989). David E. Lilienfeld, Overview of Epidemiology, 3 Shepard’s Expert & Sci. Evid. Q. 25 (1995). David E. Lilienfeld & Paul D. Stolley, Foundations of Epidemiology (3d ed. 1994). Marcello Pagano & Kimberlee Gauvreau, Principles of Biostatistics (2d ed. 2000). Pharmacoepidemiology (Brian L. Strom ed., 4th ed. 2005). Richard K. Riegelman & Robert A. Hirsch, Studying a Study and Testing a Test: How to Read the Health Science Literature (5th ed. 2005). Bernard Rosner, Fundamentals of Biostatistics (6th ed. 2006). Kenneth J. Rothman et al., Modern Epidemiology (3d ed. 2008). David A. Savitz, Interpreting Epidemiologic Evidence: Strategies for Study Design and Analysis (2003). James J. Schlesselman, Case-Control Studies: Design, Conduct, Analysis (1982). Lisa M. Sullivan, Essentials of Biostatistics (2008). Mervyn Susser, Epidemiology, Health and Society: Selected Papers (1987). References on Law and Epidemiology American Law Institute, Reporters’ Study on Enterprise Responsibility for Per- sonal Injury (1991). Bert Black & David H. Hollander, Jr., Unraveling Causation: Back to the Basics, 3 U. Balt. J. Envtl. L. 1 (1993). Bert Black & David Lilienfeld, Epidemiologic Proof in Toxic Tort Litigation, 52 Ford- ham L. Rev. 732 (1984). 630

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Reference Guide on Epidemiology Gerald Boston, A Mass-Exposure Model of Toxic Causation: The Content of Scientific Proof and the Regulatory Experience, 18 Colum. J. Envtl. L. 181 (1993). Vincent M. Brannigan et al., Risk, Statistical Inference, and the Law of Evidence: The Use of Epidemiological Data in Toxic Tort Cases, 12 Risk Analysis 343 (1992). Troyen Brennan, Causal Chains and Statistical Links: The Role of Scientific Uncertainty in Hazardous-Substance Litigation, 73 Cornell L. Rev. 469 (1988). 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. Pitt. L. Rev. 1 (1989). Philip Cole, Causality in Epidemiology, Health Policy, and Law, 27 Envtl. L. Rep. 10,279 (June 1997). Comment, Epidemiologic Proof of Probability: Implementing the Proportional Recovery Approach in Toxic Exposure Torts, 89 Dick. L. Rev. 233 (1984). George W. Conk, Against the Odds: Proving Causation of Disease with Epidemiological Evidence, 3 Shepard’s Expert & Sci. Evid. Q. 85 (1995). Carl F. Cranor, Toxic Torts: Science, Law, and the Possibility of Justice (2006). Carl F. Cranor et al., Judicial Boundary Drawing and the Need for Context-Sensitive Science in Toxic Torts After Daubert v. Merrell Dow Pharmaceuticals, Inc., 16 Va. Envtl. L.J. 1 (1996). Richard Delgado, Beyond Sindell: Relaxation of Cause-in-Fact Rules for Indeterminate Plaintiffs, 70 Cal. L. Rev. 881 (1982). Michael Dore, A Commentary on the Use of Epidemiological Evidence in Demonstrating Cause-in-Fact, 7 Harv. Envtl. L. Rev. 429 (1983). Jean Macchiaroli Eggen, Toxic Torts, Causation, and Scientific Evidence After Daubert, 55 U. Pitt. L. Rev. 889 (1994). Daniel A. Farber, Toxic Causation, 71 Minn. L. Rev. 1219 (1987). Heidi Li Feldman, Science and Uncertainty in Mass Exposure Litigation, 74 Tex. L. Rev. 1 (1995). Stephen E. Fienberg et al., Understanding and Evaluating Statistical Evidence in Litiga- tion, 36 Jurimetrics J. 1 (1995). Joseph L. Gastwirth, Statistical Reasoning in Law and Public Policy (1988). Herman J. Gibb, Epidemiology and Cancer Risk Assessment, in Fundamentals of Risk Analysis and Risk Management 23 (Vlasta Molak ed., 1997). Steve Gold, Note, Causation in Toxic Torts: Burdens of Proof, Standards of Persuasion and Statistical Evidence, 96 Yale L.J. 376 (1986). Leon Gordis, Epidemiologic Approaches for Studying Human Disease in Relation to Hazardous Waste Disposal Sites, 25 Hous. L. Rev. 837 (1988). Michael D. Green, Expert Witnesses and Sufficiency of Evidence in Toxic Substances Litigation: The Legacy of Agent Orange and Bendectin Litigation, 86 Nw. U. L. Rev. 643 (1992). Michael D. Green, The Future of Proportional Liability, in Exploring Tort Law (Stuart Madden ed., 2005). 631

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Reference Manual on Scientific Evidence Sander Greenland, The Need for Critical Appraisal of Expert Witnesses in Epidemiology and Statistics, 39 Wake Forest L. Rev. 291 (2004). Khristine L. Hall & Ellen Silbergeld, Reappraising Epidemiology: A Response to Mr. Dore, 7 Harv. Envtl. L. Rev. 441 (1983). Jay P. Kesan, Drug Development: Who Knows Where the Time Goes?: A Critical Examination of the Post-Daubert Scientific Evidence Landscape, 52 Food Drug Cosm. L.J. 225 (1997). Jay P. Kesan, An Autopsy of Scientific Evidence in a Post-Daubert World, 84 Geo. L. Rev. 1985 (1996). Constantine Kokkoris, Comment, DeLuca v. Merrell Dow Pharmaceuticals, Inc.: Statistical Significance and the Novel Scientific Technique, 58 Brook. L. Rev. 219 (1992). James P. Leape, Quantitative Risk Assessment in Regulation of Environmental Carcino- gens, 4 Harv. Envtl. L. Rev. 86 (1980). David E. Lilienfeld, Overview of Epidemiology, 3 Shepard’s Expert & Sci. Evid. Q. 23 (1995). Junius McElveen, Jr., & Pamela Eddy, Cancer and Toxic Substances: The Problem of Causation and the Use of Epidemiology, 33 Clev. St. L. Rev. 29 (1984). Modern Scientific Evidence: The Law and Science of Expert Testimony (David L. Faigman et al. eds., 2009–2010). Note, Development in the Law—Confronting the New Challenges of Scientific Evidence, 108 Harv. L. Rev. 1481 (1995). Susan R. Poulter, Science and Toxic Torts: Is There a Rational Solution to the Problem of Causation? 7 High Tech. L.J. 189 (1992). Jon Todd Powell, Comment, How to Tell the Truth with Statistics: A New Statistical Approach to Analyzing the Data in the Aftermath of Daubert v. Merrell Dow Pharmaceuticals, 31 Hous. L. Rev. 1241 (1994). Restatement (Third) of Torts: Liability for Physical and Emotional Harm § 28, cmt. c & rptrs. note (2010). David Rosenberg, The Causal Connection in Mass Exposure Cases: A Public Law Vision of the Tort System, 97 Harv. L. Rev. 849 (1984). Joseph Sanders, The Bendectin Litigation: A Case Study in the Life-Cycle of Mass Torts, 43 Hastings L.J. 301 (1992). Joseph Sanders, Scientific Validity, Admissibility, and Mass Torts After Daubert, 78 Minn. L. Rev. 1387 (1994). Joseph Sanders & Julie Machal-Fulks, The Admissibility of Differential Diagnosis to Prove Causation in Toxic Tort Cases: The Interplay of Adjective and Substantive Law, 64 L. & Contemp. Probs. 107 (2001). Palma J. Strand, The Inapplicability of Traditional Tort Analysis to Environmental Risks: The Example of Toxic Waste Pollution Victim Compensation, 35 Stan. L. Rev. 575 (1983). Richard W. Wright, Causation in Tort Law, 73 Cal. L. Rev. 1735 (1985). 632