Preventing and Treating Missing Data in Longitudinal Clinical Trials

A Practical Guide

Nonfiction, Health & Well Being, Medical, Reference, Biostatistics, Science & Nature, Mathematics
Cover of the book Preventing and Treating Missing Data in Longitudinal Clinical Trials by Craig H. Mallinckrodt, Cambridge University Press
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Author: Craig H. Mallinckrodt ISBN: 9781107301832
Publisher: Cambridge University Press Publication: January 28, 2013
Imprint: Cambridge University Press Language: English
Author: Craig H. Mallinckrodt
ISBN: 9781107301832
Publisher: Cambridge University Press
Publication: January 28, 2013
Imprint: Cambridge University Press
Language: English

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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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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