Preventing and Treating Missing Data in Longitudinal Clinical Trials: A Practical Guide (Practical Guides to Biostatistics and Epidemiology)

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9781107679153: Preventing and Treating Missing Data in Longitudinal Clinical Trials: A Practical Guide (Practical Guides to Biostatistics and Epidemiology)
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Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset.

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'... this monograph is good value, and I recommend all those involved in the design, conduct or analysis of trials to peruse a copy. Non-statisticians will inevitably be frustrated at times, but if this monograph fosters improved discussion and understanding of the issues raised by missing data in study teams - and how they might be addressed - it will have done its work. In his choice of audience Mallinckrodt set himself a high bar ... it has ... been cleared. In addition, [his] wry turn of phrase was an unexpected pleasure. You'll miss this if you don't buy it!' James R. Carpenter, Journal of Biopharmaceutical Statistics

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Focusing on the prevention and treatment of missing data in longitudinal clinical trials, this book offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset.

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