LEADER 05355nam 2200649Ia 450 001 9910143744303321 005 20170815120422.0 010 $a1-280-83950-3 010 $a9786610839506 010 $a0-470-51044-7 010 $a0-470-51043-9 035 $a(CKB)1000000000356142 035 $a(EBL)291012 035 $a(OCoLC)476048384 035 $a(SSID)ssj0000203525 035 $a(PQKBManifestationID)11183238 035 $a(PQKBTitleCode)TC0000203525 035 $a(PQKBWorkID)10173711 035 $a(PQKB)11400145 035 $a(MiAaPQ)EBC291012 035 $a(EXLCZ)991000000000356142 100 $a20061025d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMissing data in clinical studies$b[electronic resource] /$fGeert Molenberghs, Michael G. Kenward 210 $aChichester, Eng. ;$aHoboken, NJ $cJ. Wiley & Sons$dc2007 215 $a1 online resource (528 p.) 225 1 $aStatistics in practice 300 $aDescription based upon print version of record. 311 $a0-470-84981-9 320 $aIncludes bibliographical references (p. 483-496) and index. 327 $aMissing Data in Clinical Studies; Contents; Preface; Acknowledgements; I Preliminaries; 1 Introduction; 1.1 From Imbalance to the Field of Missing Data Research; 1.2 Incomplete Data in Clinical Studies; 1.3 MAR, MNAR, and Sensitivity Analysis; 1.4 Outline of the Book; 2 Key Examples; 2.1 Introduction; 2.2 The Vorozole Study; 2.3 The Orthodontic Growth Data; 2.4 Mastitis in Dairy Cattle; 2.5 The Depression Trials; 2.6 The Fluvoxamine Trial; 2.7 The Toenail Data; 2.8 Age-Related Macular Degeneration Trial; 2.9 The Analgesic Trial; 2.10 The Slovenian Public Opinion Survey 327 $a3 Terminology and Framework3.1 Modelling Incompleteness; 3.2 Terminology; 3.3 Missing Data Frameworks; 3.4 Missing Data Mechanisms; 3.5 Ignorability; 3.6 Pattern-Mixture Models; Part II Classical Techniques and the Need for Modelling; 4 A Perspective on Simple Methods; 4.1 Introduction; 4.1.1 Measurement model; 4.1.2 Method for handling missingness; 4.2 Simple Methods; 4.2.1 Complete case analysis; 4.2.2 Imputation methods; 4.2.3 Last observation carried forward; 4.3 Problems with Complete Case Analysis and Last Observation Carried Forward 327 $a4.4 Using the Available Cases: a Frequentist versus a Likelihood Perspective4.4.1 A bivariate normal population; 4.4.2 An incomplete contingency table; 4.5 Intention to Treat; 4.6 Concluding Remarks; 5 Analysis of the Orthodontic Growth Data; 5.1 Introduction and Models; 5.2 The Original, Complete Data; 5.3 Direct Likelihood; 5.4 Comparison of Analyses; 5.5 Example SAS Code for Multivariate Linear Models; 5.6 Comparative Power under Different Covariance Structures; 5.7 Concluding Remarks; 6 Analysis of the Depression Trials; 6.1 View 1: Longitudinal Analysis 327 $a6.2 Views 2a and 2b and All versus Two Treatment ArmsIII Missing at Random and Ignorability; 7 The Direct Likelihood Method; 7.1 Introduction; 7.2 Ignorable Analyses in Practice; 7.3 The Linear Mixed Model; 7.4 Analysis of the Toenail Data; 7.5 The Generalized Linear Mixed Model; 7.6 The Depression Trials; 7.7 The Analgesic Trial; 8 The Expectation-Maximization Algorithm; 8.1 Introduction; 8.2 The Algorithm; 8.2.1 The initial step; 8.2.2 The E step; 8.2.3 The M step; 8.3 Missing Information; 8.4 Rate of Convergence; 8.5 EM Acceleration; 8.6 Calculation of Precision Estimates 327 $a8.7 A Simple Illustration8.8 Concluding Remarks; 9 Multiple Imputation; 9.1 Introduction; 9.2 The Basic Procedure; 9.3 Theoretical Justification; 9.4 Inference under Multiple Imputation; 9.5 Efficiency; 9.6 Making Proper Imputations; 9.7 Some Roles for Multiple Imputation; 9.8 Concluding Remarks; 10 Weighted Estimating Equations; 10.1 Introduction; 10.2 Inverse Probability Weighting; 10.3 Generalized Estimating Equations for Marginal Models; 10.3.1 Marginal models for non-normal data; 10.3.2 Generalized estimating equations; 10.3.3 A method based on linearization 327 $a10.4 Weighted Generalized Estimating Equations 330 $aMissing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described. Provides a practical guide to the analysis of clinical trials and related studies with missing data.Examines 410 0$aStatistics in practice. 606 $aClinical trials$xStatistical methods 606 $aMissing observations (Statistics) 608 $aElectronic books. 615 0$aClinical trials$xStatistical methods. 615 0$aMissing observations (Statistics) 676 $a610.724 700 $aMolenberghs$b Geert$0259367 701 $aKenward$b Michael G.$f1956-$0102987 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143744303321 996 $aMissing data in clinical studies$92248133 997 $aUNINA