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157 black & white illustrations, 233 black & white tables
3rd Revised edition
Table Of Contents
FUNDAMENTAL ISSUES What is Epidemiology? Case Studies: The Work of Doll and Hill Populations and Samples Measuring Disease Measuring the Risk Factor Causality Studies Using Routine Data Study Design Data Analysis Exercises BASIC ANALYTICAL PROCEDURES Introduction Case Study Types of Variables Tables and Charts Inferential Techniques for Categorical Variables Descriptive Techniques for Quantitative Variables Inferences about Means Inferential Techniques for Non-Normal Data Measuring Agreement Assessing Diagnostic Tests Exercises ASSESSING RISK FACTORS Risk and Relative Risk Odds and Odds Ratio Relative Risk or Odds Ratio? Prevalence Studies Testing Association Risk Factors Measured at Several Levels Attributable Risk Rate and Relative Rate Measures of Difference EPITAB Commands in Stata Exercises CONFOUNDING AND INTERACTION Introduction The Concept of Confounding Identification of Confounders Assessing Confounding Standardization Mantel-Haenszel Methods The Concept of Interaction Testing for Interaction Dealing with Interaction EPITAB Commands in Stata Exercises COHORT STUDIES Design Considerations Analytical Considerations Cohort Life Tables Kaplan-Meier Estimation Comparison of Two Sets of Survival Probabilities Competing Risk The Person-Years Method Period-Cohort Analysis Exercises CASE-CONTROL STUDIES Basic Design Concepts Basic Methods of Analysis Selection of Cases Selection of Controls Matching The Analysis of Matched Studies Nested Case-Control Studies Case-Cohort Studies Case-Crossover Studies Exercises INTERVENTION STUDIES Introduction Ethical Considerations Avoidance of Bias Parallel Group Studies Cross-Over Studies Sequential Studies Allocation to Treatment Group Trials as Cohorts Exercises SAMPLE SIZE DETERMINATION Introduction Power Testing a Mean Value Testing a Difference between Means Testing a Proportion Testing a Relative Risk Case-Control Studies Complex Sampling Designs Concluding Remarks Exercises MODELING QUANTITATIVE OUTCOME VARIABLES Statistical Models One Categorical Explanatory Variable One Quantitative Explanatory Variable Two Categorical Explanatory Variables Model Building General Linear Models Several Explanatory Variables Model Checking Confounding Splines Panel Data Non-Normal Alternatives Exercises MODELING BINARY OUTCOME DATA Introduction Problems with Standard Regression Models Logistic Regression Interpretation of Logistic Regression Coefficients Generic Data Multiple Logistic Regression Models Tests of Hypotheses Confounding Interaction Dealing with a Quantitative Explanatory Variable Model Checking Measurement Error Case-Control Studies Outcomes with Several Levels Longitudinal Data Binomial Regression Propensity Scoring Exercises MODELING FOLLOW-UP DATA Introduction Basic Functions of Survival Time Estimating the Hazard Function Probability Models Proportional Hazards Regression Models The Cox Proportional Hazards Model The Weibull Proportional Hazards Model Model Checking Competing Risk Poisson Regression Pooled Logistic Regression Exercises META-ANALYSIS Reviewing Evidence Systematic Review A General Approach to Pooling Investigating Heterogeneity Pooling Tabular Data Individual Participant Data Dealing with Aspects of Study Quality Publication Bias Advantages and Limitations of Meta-Analysis Exercises RISK SCORES AND CLINICAL DECISION RULES Introduction Association and Prognosis Risk Scores from Statistical Models Quantifying Discrimination Calibration Recalibration The Accuracy of Predictions Assessing an Extraneous Prognostic Variable Reclassification Validation Presentation of Risk Scores Impact Studies Exercises COMPUTER-INTENSIVE METHODS Rationale The Bootstrap Bootstrap Confidence Intervals Practical Issues When Bootstrapping Further Examples of Bootstrapping Bootstrap Hypothesis Testing Limitations of Bootstrapping Permutation Tests Missing Values Naive Imputation Methods Univariate Multiple Imputation Multivariate Multiple Imputatio
Mark Woodward is a professor of statistics and epidemiology at the University of Oxford, a professor of biostatistics in the George Institute at the University of Sydney, and an adjunct professor of epidemiology at Johns Hopkins University.