Interpreting Epidemiologic Evidence

International Journal of Epidemiology Highly relevant to any practicing epidemiologist.

JAMA 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.

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 Required reading for the novice and the professional

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.

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 Association