Interpreting Epidemiologic Evidence: Connecting Research to Applications

Epidemiology, the so-called "science of public health," has undergone a boom in the last decade as public interest and engagement in population health has skyrocketed. While this boom has done much to spark advances in the technology of epidemiology, it has also made it harder for those who want to use epidemiology to guide policy and clinical practice to fully appreciate the meaning of the research findings. Interpreting Epidemiologic Evidence offers those who have had an introductory course in epidemiology the knowledge they need to make clear connections from research findings to practical applications. Written in clear and lively prose, it empowers students at all levels to evaluate a study's design, implementation, and ultimate findings, giving the guidance needed to apply the information appropriately. Liberal use of practical examples serves both to illustrate core concepts and to motivate readers to think critically about the causal connections that population health studies aim to explore. Completely revised and updated, this new edition of Interpreting Epidemiologic Evidence is an invaluable core text for both epidemiologists in training and practitioners across other disciplines with even an introductory knowledge of epidemiology.

Table of Contents:
Interpreting Epidemiologic Evidence: Connecting Research to Applications 1. Introduction Synopsis Learning Objectives Perspective Approach to the Evaluation of Evidence Organization of Book 2. The Nature of Epidemiologic Evidence Synopsis Learning Objectives Goals of Epidemiologic Research Measurement of Causal Relations Between Exposure and Disease Applications of Epidemiologic Research Framework for Examining Epidemiologic Evidence Relationship of Epidemiology to Health Policy Exercise: Critical Assessment of Study Methods, Results, and Applications 3. Causal Diagrams for Epidemiologic Inference Synopsis Learning Objectives Introduction Causal Diagrams in Epidemiology Purpose and Terminology DAGs Encode Our Assumptions Statistical Associations Connection to Data Analyses Depicting Passage of Time Direct vs. Indirect Effects Concluding Thoughts Recommended Additional Readings Exercise: Application of Causal Diagrams for Epidemiologic Inference 4. Strategy for Drawing Inferences from Epidemiologic Evidence Synopsis Learning Objectives Conceptual Framework for the Evaluation of Error Estimation of Measures of Association Systematic Evaluation of Sources of Error Objective Evaluation of Sources of Error Identifying the Most Important Sources of Error Specifying Bias Scenarios Exercise: Specifying Scenarios of Bias 5. Confounding I: Theoretical Considerations Synopsis Learning Objectives Definition Identifying Potential Confounders Traditional Approach to Assessing Confounding Modern Approach to Assessing Confounding Inappropriate Adjustments Assessing the Direction and Magnitude of Potential Confounding Methods of Controlling Confounding Randomization Selection of Study Setting Free of Confounding Restrict Study Groups to Enhance Comparability Statistical Adjustment for Confounding Recommended Additional Readings Exercise: Conceptual Basis of Confounding 6. Confounding II: Practical Considerations Synopsis Learning Objectives Evaluating the Presence and Impact of Confounding Specifying Scenarios of Confounding Assessing Whether Confounding is Present Consider Potential for Complete Confounding Assess Consequences of Inaccurate Confounder Measurement Applying Knowledge of Confounding Based on Other Studies Assessing Confounding When Risk Factors are Unknown Dose-Response Gradients and Potential for Confounding Integrated Assessment of Potential Confounding Exercise: Connecting Conceptual and Statistical Assessment of Confounding 7. Selection Bias and Confounding Resulting from Selection in Cohort Studies Synopsis Learning Objectives Study Designs Definition and Examples of Selection Bias Selection Bias Versus Confounding Evaluation of Bias in Cohort Studies Compare Those Included to Those Not Included Compare Disease Rates Among Unexposed to External Populations Assess Whether Expected Patterns of Disease are Present Assess Pattern of Results in Relation to Markers of Susceptibility to Bias Due to Participant Selection Assess Rates for Diseases Known Not to Be Affected by the Exposure Integrated Assessment of Potential for Bias in Cohort Studies Exercise: Assessment of Bias Due to Selection in Cohort Studies 8. Selection Bias in Case-Control Studies Synopsis Learning Objectives Control Selection Participant Selection in Case-Control and Cohort Studies Selection of Controls from the Source Population Coherence of Cases and Controls Evaluation of Selection Bias in Case-Control Studies Temporal Coherence of Cases and Controls Discretionary Health Care of Cases and Controls Compare Exposure Prevalence in Controls to an External Population Determine Whether Exposure Prevalence Varies as Expected Among Controls Examine Markers of Potential Selection Bias in Relation to Measures of Association Adjust Measures of Association for Known Sources of Non- Comparability Determine Whether Established Associations Can Be Confirmed Integrated Assessment of Potential for Selection Bias in Case-Control Studies Exercise: Assessing Selection Bias in Case-Control Studies 9. Bias Due to Loss of Study Participants Synopsis Learning Objectives Conceptual Framework for Examining Bias Due to Loss of Study Participants Evaluation of Bias Due to Loss of Study Participants Characterize Nonparticipants Consider Gradient of Difficulty in Recruitment Stratify Study Base by Markers of Participation Impute Information for Nonparticipants Integrated Assessment of Potential for Bias Due to Loss of Study Participants Exercise: Examining Implications of Non-Participation 10. Measurement and Classification of Exposure Synopsis Learning Objectives Introduction Ideal Versus Operational Measures of Exposure Biologically Relevant Exposure Temporally Relevant Exposure Optimal Level of Exposure Aggregation Comparison of Optimal to Operational Measures of Exposure Does Exposure Misclassification Differ by Disease Status? Definitions Mechanisms of Differential Exposure Misclassification Evaluation of Exposure Misclassification Compare Routine Measure to Superior Measures Examine Multiple Indicators of Exposure Examine Subsets of the Population with Differing Exposure Data Quality Evaluate Known Predictors of Exposure Evaluate Known Consequences of Exposure Examine Dose-Response Gradients Evaluate Whether Exposure Misclassification Differs by Disease Status Identification of Subgroups with Nondifferential Exposure Misclassification Integrated Assessment of Bias Due to Exposure Misclassification Exercise: Assessing the Presence and Impact of Exposure Misclassification 11. Measurement and Classification of Disease Synopsis Learning Objectives Framework for Evaluating Disease Misclassification Sources of Disease Misclassification Impact of Differential and Nondifferential Disease Misclassification Evaluation of Disease Misclassification Verify Diagnostic Accuracy for Subset of Study Participants Examine Results Across Levels of Diagnostic Certainty Evaluate Alternate Methods of Disease Grouping Determine Whether Misclassification is Differential by Exposure Status Create Subgroups with Accurate Ascertainment or Non-Differential Underascertainment Restrict Inference to Disease Outcome That Can Be Ascertained Accurately Integrated Assessment of Potential for Bias Due to Disease Misclassification Exercise: Assessing the Presence and Impact of Disease Misclassification 12. Random Error Synopsis Learning Objectives Nature of Random Variation Sequential Approach to Considering Random and Systematic Error Special Considerations in Evaluating Random Error in Observational Studies Statistical Significance Testing Interpretation of Confidence Intervals Multiple Comparisons and Related Issues Integrated Assessment of Random Error Exercise: Assessing Random Error 13. Integration of Evidence Across Studies Synopsis Learning Objectives Introduction Systematic Evidence Reviews Data Pooling and Comparative Analyses Meta-Analysis Interpreting Consistency and Inconsistency Among Studies Inconsistent Findings Consistent Findings Evolution of Epidemiologic Research Integrated Assessment from Combining Evidence Across Studies Exercise: Interpreting Evidence from a Collection of Studies 14. Characterization and Communication of Conclusions Synopsis Learning Objectives Presenting Clear, Objective, and Informed Conclusions Applications of Epidemiology Integration of Epidemiologic Evidence with Other Information Identification of Key Concerns Controversy over Interpretation The Case Against Algorithms for Interpreting Epidemiologic Evidence Exercise: Communicating Summary Assessment of Epidemiologic Evidence

About the Author :
David A. Savitz, PhD, is Professor of Epidemiology and Obstetrics and Gynecology at Brown University. He has held leadership positions at the University of North Carolina (Chair of the Department of Epidemiology) and at Brown University (Vice President for Research), as well as in a number of professional organizations. His research is focused primarily on reproductive and environmental epidemiology. He was elected to the National Academy of Medicine in 2007. Gregory A. Wellenius, ScD, is Associate Professor of Epidemiology at Brown University. His research focuses on the effects of our environment on cardiovascular health, with an emphasis on the adverse health effects of air pollution and the built environment. He has taught advanced graduate courses in epidemiologic methods, provided invited expert testimony before the US House of Representatives and US Senate, and mentored a number of undergraduate, medical, masters, and doctoral students.

Review :
"Highly relevant to any practicing epidemiologist." --International Journal of Epidemiology "In departments with active research and graduate-level studies, the Savitz book will be tattered and torn well before the basic text shows signs of wear." --JAMA "Required reading for the novice and the professional -- and a primer for policymakers who need to use epidemiologic results in making public health policy decisions. it will make anyone committed to the field a better epidemioligist." --Epidemiology