LEADER 05140nam 2200589 450 001 9910464489103321 005 20200520144314.0 010 $a1-118-57363-3 035 $a(CKB)3710000000113825 035 $a(EBL)1691883 035 $a(MiAaPQ)EBC1691883 035 $a(Au-PeEL)EBL1691883 035 $a(CaPaEBR)ebr10874726 035 $a(CaONFJC)MIL611516 035 $a(OCoLC)864808991 035 $a(EXLCZ)993710000000113825 100 $a20140605h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aApplied missing data analysis in the health sciences /$fXiao-Hua Zhou [and three others] 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons,$d2014. 210 4$dİ2014 215 $a1 online resource (254 p.) 225 1 $aWiley Series in Statistics in Practice 300 $aDescription based upon print version of record. 311 $a0-470-52381-6 320 $aIncludes bibliographical references and index. 327 $aApplied Missing Data Analysis in the Health Sciences; Contents; List of Figures; List of Tables; Preface; 1 Missing Data Concepts and Motivating Examples; 1.1 Overview of the Missing Data Problem; 1.2 Patterns and Mechanisms of Missing Data; 1.2.1 Missing Data Patterns; 1.2.2 Missing Data Mechanisms; 1.3 Data Examples; 1.3.1 Improving Mood and Promoting Access to Collaborative Treatment (IMPACT) Study; 1.3.2 National Alzheimer's Coordinating Center Minimum DataSet; 1.3.3 National Alzheimer's Coordinating Center Uniform DataSet; 1.3.4 The Pathways Study 327 $a1.3.5 Randomized Trial on Vitamin A Supplement1.3.6 Randomized Trial on Effectiveness of Flu Shot; 2 Overview of Methods for Dealing with Missing Data; 2.1 Methods That Remove Observations; 2.1.1 Complete-Case Methods; 2.1.2 Weighted Complete-Case Methods; 2.1.3 Removing Variables with Large Amounts of Missing Values; 2.2 Methods That Utilize All Available Data; 2.2.1 Maximum Likelihood; 2.3 Methods That Impute Missing Values; 2.3.1 Single Imputation Methods; 2.3.2 Multiple Imputation; 2.4 Bayesian Methods; 3 Design Considerations in the Presence of Missing Data 327 $a3.1 Design Factors Related to Missing Data3.2 Strategies for Limiting Missing Data in the Design of Clinical Trials; 3.3 Strategies for Limiting Missing Data in the Conduct of Clinical Trials; 3.4 Minimize the Impact of Missing Data; 4 Cross-Sectional Data Methods; 4.1 Overview of General Methods; 4.2 Data Examples; 4.2.1 Simulation Study; 4.2.2 NHANES Example; 4.3 Maximum Likelihood Approach; 4.3.1 EM Algorithm for Linear Regression with a Missing Continuous Covariate; 4.3.2 EM Algorithm for Linear Regression with Missing Discrete Covariate 327 $a4.3.3 EM Algorithm for Logistic Regression with Missing Binary Outcome4.3.4 Simulation Study; 4.3.5 IMPACT Study; 4.3.6 NACC Study; 4.4 Bayesian Methods; 4.4.1 Theory; 4.4.2 Joint Model and Ignorable Missingness; 4.4.3 Bayesian Computation for Missing Data; 4.4.4 Simulation Example; 4.4.5 IMPACT Study; 4.4.6 NHANES Example; 4.5 Multiple Imputation; 4.5.1 Theory; 4.5.2 Some General Guidelines on Imputation Models and Analysis Models; 4.5.3 Theoretical Justification for the MI Method; 4.5.4 MI When ? Is ?-Dimensional; 4.5.5 Simulated Example; 4.5.6 IMPACT Study 327 $a4.6 Imputing Estimating Equations4.7 Inverse Probability Weighting; 4.7.1 Theory; 4.7.2 Simulated Example; 4.8 Doubly Robust Estimators; 4.8.1 Theory; 4.8.2 Variance Estimation; 4.8.3 NACC Study; 4.9 Code Used in This Chapter; 4.9.1 Code Used in Section 4.3.4; 4.9.2 Code Used in Section 4.3.5; 4.9.3 Code Used in Section 4.4.4; 4.9.4 Code Used in Section 4.4.5; 4.9.5 Code Used in Section 4.4.6; 4.9.6 Code Used in Section 4.5.5; 4.9.7 Code Used in Section 4.5.6; 4.9.8 Code Used in Section 4.7.2; 5 Longitudinal Data Methods; 5.1 Overview; 5.2 Examples; 5.2.1 IMPACT Study; 5.2.2 NACC UDS Data 327 $a5.3 Longitudinal Regression Models for Complete Data 330 $a A modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various modern statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference methods and the field of diagnostic medicine. Organized by types of data, chapter coverage 410 0$aStatistics in practice. 606 $aMedical sciences$xStudy and teaching 606 $aMedicine$xResearch 608 $aElectronic books. 615 0$aMedical sciences$xStudy and teaching. 615 0$aMedicine$xResearch. 676 $a610.711 702 $aZhou$b Xiao-Hua 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910464489103321 996 $aApplied missing data analysis in the health sciences$91920119 997 $aUNINA