Description
The detrimental effects of incomplete data sets on the results of clinical trials are both well known and all too commonly recurrent. It is essential that the correct statistical methodology be applied in order to effectively analyse the results of trials affected by missing data. Missing Data in Clinical Trials 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 the problems caused by missing data, enabling a complete understanding of how to overcome them. * Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism. * Illustrated throughout with real-life case studies and worked examples from clinical trials. * Details the use and implementation of the necessary statistical software, primarily SAS. Missing Data in Clinical Trials has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit. Preface. Acknowledgements. I Preliminaries. 1 Introduction. 2 Key Examples. 3 Terminology and Framework. II Classical Techniques and the Need for Modelling. 4 A Perspective on Simple Methods. 5 Analysis of the Orthodontic Growth Data. 6 Analysis of the Depression Trials. III Missing at Random and Ignorability. 7 The Direct Likelihood Method. 8 The Expectation-Maximization Algorithm. 9 Multiple Imputation. 10 Weighted Estimating Equations. 11 Combining GEE and MI. 12 Likelihood-Based Frequentist Inference. 13 Analysis of the Age-Related Macular Degeneration Trial. 14 Incomplete Data and SAS. IV Missing Not at Random. 15 Selection Models. 16 Pattern-Mixture Models. 17 Shared-Parameter Models. 18 Protective Estimation. V Sensitivity Analysis. 19 MNAR, MAR, and the Nature of Sensitivity. 20 Sensitivity Happens. 21 Regions of Ignorance and Uncertainty. 22 Local and Global Influence Methods. 23 The Nature of Local Influence. 24 A Latent-Class Mixture Model for Incomplete Longitudinal Gaussian Data. VI Case Studies. 25 The Age-Related Macular Degeneration Trial. 26 The Vorozole Study. References. Index.
