Epidemiology: Study Design and Data Analysis, Third Edition

ISBN-13: 9781439839706 / Angielski / Twarda / 2013 / 898 str.

Mark Woodward

Opis

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

Spis treści

FUNDAMENTAL ISSUESWhat is Epidemiology?Case Studies: The Work of Doll and HillPopulations and SamplesMeasuring DiseaseMeasuring the Risk FactorCausalityStudies Using Routine DataStudy DesignData AnalysisExercisesBASIC ANALYTICAL PROCEDURESIntroductionCase StudyTypes of VariablesTables and ChartsInferential Techniques for Categorical VariablesDescriptive Techniques for Quantitative VariablesInferences about MeansInferential Techniques for Non-Normal DataMeasuring AgreementAssessing Diagnostic TestsExercisesASSESSING RISK FACTORSRisk and Relative RiskOdds and Odds RatioRelative Risk or Odds Ratio?Prevalence StudiesTesting AssociationRisk Factors Measured at Several LevelsAttributable RiskRate and Relative RateMeasures of DifferenceEPITAB Commands in StataExercisesCONFOUNDING AND INTERACTIONIntroductionThe Concept of ConfoundingIdentification of ConfoundersAssessing ConfoundingStandardizationMantel-Haenszel MethodsThe Concept of InteractionTesting for InteractionDealing with InteractionEPITAB Commands in StataExercisesCOHORT STUDIESDesign ConsiderationsAnalytical ConsiderationsCohort Life TablesKaplan-Meier EstimationComparison of Two Sets of Survival ProbabilitiesCompeting RiskThe Person-Years MethodPeriod-Cohort AnalysisExercisesCASE-CONTROL STUDIESBasic Design ConceptsBasic Methods of AnalysisSelection of CasesSelection of ControlsMatchingThe Analysis of Matched StudiesNested Case-Control StudiesCase-Cohort StudiesCase-Crossover StudiesExercisesINTERVENTION STUDIESIntroductionEthical Considerations Avoidance of BiasParallel Group StudiesCross-Over StudiesSequential StudiesAllocation to Treatment GroupTrials as CohortsExercisesSAMPLE SIZE DETERMINATIONIntroductionPowerTesting a Mean ValueTesting a Difference between Means Testing a ProportionTesting a Relative RiskCase-Control Studies Complex Sampling Designs Concluding RemarksExercisesMODELING QUANTITATIVE OUTCOME VARIABLESStatistical ModelsOne Categorical Explanatory VariableOne Quantitative Explanatory VariableTwo Categorical Explanatory VariablesModel BuildingGeneral Linear ModelsSeveral Explanatory VariablesModel CheckingConfoundingSplinesPanelDataNon-Normal AlternativesExercisesMODELING BINARY OUTCOME DATAIntroductionProblems with Standard Regression ModelsLogistic RegressionInterpretation of Logistic Regression CoefficientsGeneric DataMultiple Logistic Regression ModelsTests of HypothesesConfoundingInteractionDealing with a Quantitative Explanatory VariableModel CheckingMeasurement ErrorCase-Control StudiesOutcomes with Several LevelsLongitudinal Data Binomial RegressionPropensity ScoringExercisesMODELING FOLLOW-UP DATAIntroductionBasic Functions of Survival TimeEstimating the Hazard FunctionProbability ModelsProportional Hazards Regression ModelsThe Cox Proportional Hazards ModelThe Weibull Proportional Hazards ModelModel CheckingCompeting RiskPoisson RegressionPooled Logistic RegressionExercisesMETA-ANALYSISReviewing EvidenceSystematic ReviewA General Approach to PoolingInvestigating HeterogeneityPooling Tabular DataIndividual Participant DataDealing with Aspects of Study QualityPublication BiasAdvantages and Limitations of Meta-Analysis Exercises RISK SCORES AND CLINICAL DECISION RULESIntroduction Association and Prognosis Risk Scores from Statistical Models Quantifying Discrimination Calibration RecalibrationThe Accuracy of Predictions Assessing an Extraneous Prognostic Variable Reclassification Validation Presentation of Risk Scores Impact StudiesExercises COMPUTER-INTENSIVE METHODSRationale 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 MethodsUnivariate Multiple ImputationMultivariate Multiple ImputationWhen 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

O autorze

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.