LEADER 06021nam 2200697 450 001 9910819122203321 005 20200520144314.0 010 $a1-118-76253-3 010 $a1-118-76251-7 010 $a1-118-76250-9 035 $a(CKB)3710000000089668 035 $a(EBL)1636082 035 $a(SSID)ssj0001111588 035 $a(PQKBManifestationID)11636865 035 $a(PQKBTitleCode)TC0001111588 035 $a(PQKBWorkID)11156372 035 $a(PQKB)10836172 035 $a(OCoLC)861789127 035 $a(MiAaPQ)EBC1636082 035 $a(DLC) 2013043089 035 $a(Au-PeEL)EBL1636082 035 $a(CaPaEBR)ebr10839238 035 $a(CaONFJC)MIL578562 035 $a(OCoLC)870951046 035 $a(PPN)192309072 035 $a(EXLCZ)993710000000089668 100 $a20131029d2014 uy| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aClinical trials with missing data $ea guide for practitioners /$fMichael O'Kelly, Bohdana Ratitch 210 1$aChichester, West Sussex :$cJohn Wiley & Sons Inc.,$d2014. 215 $a1 online resource (473 p.) 225 0 $aStatistics in practice 300 $aDescription based upon print version of record. 311 $a1-118-46070-7 320 $aIncludes bibliographical references and index. 327 $aClinical Trials with Missing Data; Contents; Preface; References; Acknowledgments; Notation; Table of SAS code fragments; Contributors; 1 Whats the problem with missing data?; 1.1 What do we mean by missing data?; 1.1.1 Monotone and non-monotone missing data; 1.1.2 Modeling missingness, modeling the missing value and ignorability; 1.1.3 Types of missingness (MCAR, MAR and MNAR); 1.1.4 Missing data and study objectives; 1.2 An illustration; 1.3 Why cant I use only the available primary endpoint data?; 1.4 Whats the problem with using last observation carried forward? 327 $a1.5 Can we just assume that data are missing at random?1.6 What can be done if data may be missing not at random?; 1.7 Stress-testing study results for robustness to missing data; 1.8 How the pattern of dropouts can bias the outcome; 1.9 How do we formulate a strategy for missing data?; 1.10 Description of example datasets; 1.10.1 Example dataset in Parkinsons disease treatment; 1.10.2 Example dataset in insomnia treatment; 1.10.3 Example dataset in mania treatment; Appendix 1.A: Formal definitions of MCAR, MAR and MNAR; References; 2 The prevention of missing data; 2.1 Introduction 327 $a2.2 The impact of "too much" missing data 2.2.1 Example from human immunodeficiency virus; 2.2.2 Example from acute coronary syndrome; 2.2.3 Example from studies in pain; 2.3 The role of the statistician in the prevention of missing data; 2.3.1 Illustrative example from HIV; 2.4 Methods for increasing subject retention; 2.5 Improving understanding of reasons for subject withdrawal; Acknowledgments; Appendix 2.A: Example protocol text for missing data prevention; Section X Subject retention; References; 3 Regulatory guidance - a quicktour 327 $a3.1 International conference on harmonization guideline: Statistical principles for clinical trials: E93.2 The US and EU regulatory documents; 3.3 Key points in the regulatory documents on missing data; 3.4 Regulatory guidance on particular statistical approaches; 3.4.1 Available cases; 3.4.2 Single imputation methods; 3.4.3 Methods that generally assume MAR; 3.4.4 Methods that are used assuming MNAR; 3.5 Guidance about how to plan for missing data in a study; 3.6 Differences in emphasis between the NRC report and EU guidance documents; 3.6.1 The term "conservative" 327 $a3.6.2 Last observation carried forward 3.6.3 Post hoc analyses; 3.6.4 Non-monotone or intermittently missing data; 3.6.5 Assumptions should be readily interpretable; 3.6.6 Study report; 3.6.7 Training; 3.7 Other technical points from the NRC report; 3.7.1 Time-to-event analyses; 3.7.2 Tipping point sensitivity analyses; 3.8 Other US/EU/international guidance documents that refer to missing data; 3.8.1 Committee for medicinal products for human use guideline on anti-cancer products, recommendations on survival analysis 327 $a3.8.2 US guidance on considerations when research supported by office of human research protections is discontinued 330 $a"This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable. The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively. The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included - the reader is given a toolbox for implementing analyses under a variety of assumptions"--Provided by publisher. 410 0$aStatistics in Practice 606 $aClinical trials 606 $aClinical trials$xStatistical methods 615 0$aClinical trials. 615 0$aClinical trials$xStatistical methods. 676 $a610.72/4 700 $aO'Kelly$b Michael$01714097 701 $aRatitch$b Bohdana$01714098 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910819122203321 996 $aClinical trials with missing data$94107622 997 $aUNINA