Includes bibliographical references and index.

Series; Title Page; Copyright; Dedication; Preface; References; Acknowledgments; Notation; Table of SAS code fragments; Contributors; Chapter 1: What's the problem with missing data?; 1.1 What do we mean by missing data?; 1.2 An illustration; 1.3 Why can't I use only the available primary endpoint data?; 1.4 What's the problem with using last observation carried forward?; 1.5 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 outcome1.9 How do we formulate a strategy for missing data?; 1.10 Description of example datasets; Appendix 1.A: Formal definitions of MCAR, MAR and MNAR; References; Chapter 2: The prevention of missing data; 2.1 Introduction; 2.2 The impact of "too much" missing data; 2.3 The role of the statistician in the prevention of missing data; 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; References

Chapter 3: Regulatory guidance -- a quick tour3.1 International conference on harmonization guideline: Statistical principles for clinical trials: E9; 3.2 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; 3.5 Guidance about how to plan for missing data in a study; 3.6 Differences in emphasis between the NRC report and EU guidance documents; 3.7 Other technical points from the NRC report; 3.8 Other US/EU/international guidance documents that refer to missing data; 3.9 And in practice?

5.4 Applying the mixed model for repeated measures5.5 Additional mixed model for repeated measures topics; 5.6 Logistic regression mixed model for repeated measures using the generalized linear mixed model; References; Table of SAS Code Fragments; Chapter 6: Multiple imputation; 6.1 Introduction; 6.2 Imputation phase; 6.3 Analysis phase: Analyzing multiple imputed datasets; 6.4 Pooling phase: Combining results from multiple datasets; 6.5 Required number of imputations; 6.6 Some practical considerations; 6.7 Pre-specifying details of analysis with multiple imputation

"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.

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