Epidemiology: Study Design and Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science 3rd New edition)

Epidemiology: Study Design and Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science 3rd New edition)

By: Mark Woodward (author)Hardback

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Description

Highly praised for its broad, practical coverage, the second edition of this popular text incorporated the major statistical models and issues relevant to epidemiological studies. Epidemiology: Study Design and Data Analysis, Third Edition continues to focus on the quantitative aspects of epidemiological research. Updated and expanded, this edition shows students how statistical principles and techniques can help solve epidemiological problems. New to the Third Edition New chapter on risk scores and clinical decision rules New chapter on computer-intensive methods, including the bootstrap, permutation tests, and missing value imputation New sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines Many more exercises and examples using both Stata and SAS More than 60 new figures After introducing study design and reviewing all the standard methods, this self-contained book takes students through analytical methods for both general and specific epidemiological study designs, including cohort, case-control, and intervention studies. In addition to classical methods, it now covers modern methods that exploit the enormous power of contemporary computers. The book also addresses the problem of determining the appropriate size for a study, discusses statistical modeling in epidemiology, covers methods for comparing and summarizing the evidence from several studies, and explains how to use statistical models in risk forecasting and assessing new biomarkers. The author illustrates the techniques with numerous real-world examples and interprets results in a practical way. He also includes an extensive list of references for further reading along with exercises to reinforce understanding. Web Resource A wealth of supporting material can be downloaded from the book's CRC Press web page, including: Real-life data sets used in the text SAS and Stata programs used for examples in the text SAS and Stata programs for special techniques covered Sample size spreadsheet

About Author

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.

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 Imputation When Is It Worth Imputing? Exercises Appendix A: Materials Available on the Website for This Book Appendix B: Statistical Tables Appendix C: Additional Data Sets for Exercises Index

Product Details

  • ISBN13: 9781439839706
  • Format: Hardback
  • Number Of Pages: 898
  • ID: 9781439839706
  • weight: 1701
  • ISBN10: 1439839700
  • edition: 3rd New edition

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