LEADER 03867oam 2200457I 450 001 9910155108403321 005 20230808200836.0 010 $a1-351-73768-6 010 $a1-315-18663-2 010 $a1-351-73769-4 024 7 $a10.1201/9781315186634 035 $a(CKB)3710000000973138 035 $a(MiAaPQ)EBC4769135 035 $a(OCoLC)965905270 035 $a(EXLCZ)993710000000973138 100 $a20180420d2016 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aAnalyzing longitudinal clinical trial data $ea practical guide /$fby Craig Mallinckrodt and Ilya Lipkovich 210 1$aBoca Raton, FL :$cChapman and Hall/CRC, an imprint of Taylor and Francis,$d2016. 215 $a1 online resource (330 pages) $cillustrations, tables 225 1 $aChapman & Hall/CRC Biostatistics Series 311 $a1-4987-6531-9 320 $aIncludes bibliographical references and index. 327 $aCover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- List of Tables -- List of Figures -- List of Code Fragments -- Section I: Background and Setting -- 1: Introduction -- 2: Objectives and Estimands?Determining What to Estimate -- 3: Study Design?Collecting the Intended Data -- 4: Example Data -- 5: Mixed-Effects Models Review -- Section II: Modeling the Observed Data -- 6: Choice of Dependent Variable and Statistical Test -- 7: Modeling Covariance (Correlation) -- 8: Modeling Means Over Time -- 9: Accounting for Covariates -- 10: Categorical Data -- 11: Model Checking and Verification -- Section III: Methods for Dealing with Missing Data -- 12: Overview of Missing Data -- 13: Simple and Ad Hoc Approaches for Dealing with Missing Data -- 14: Direct Maximum Likelihood -- 15: Multiple Imputation -- 16: Inverse Probability Weighted Generalized Estimated Equations -- 17: Doubly Robust Methods -- 18: MNAR Methods -- 19: Methods for Incomplete Categorical Data -- Section IV: A Comprehensive Approach to Study Development and Analyses -- 20: Developing Statistical Analysis Plans -- 21: Example Analyses of Clinical Trial Data -- References -- Index. 330 3 $aAnalyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice. The book, with its example-oriented approach that includes numerous SAS and R code fragments, is an essential resource for statisticians and graduate students specializing in medical research. The authors provide clear descriptions of the relevant statistical theory and illustrate practical considerations for modeling longitudinal data. Topics covered include choice of endpoint and statistical test; modeling means and the correlations between repeated measurements; accounting for covariates; modeling categorical data; model verification; methods for incomplete (missing) data that includes the latest developments in sensitivity analyses, along with approaches for and issues in choosing estimands; and means for preventing missing data. Each chapter stands alone in its coverage of a topic. The concluding chapters provide detailed advice on how to integrate these independent topics into an over-arching study development process and statistical analysis plan. 410 0$aChapman & Hall/CRC biostatistics series. 606 $aClinical trials$vLongitudinal studies 615 0$aClinical trials 676 $a615.5072/4 700 $aMallinckrodt$b Craig H.$f1958,$01212156 702 $aLipkovich$b Ilya 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910155108403321 996 $aAnalyzing longitudinal clinical trial data$92799011 997 $aUNINA