Craig H. Mallinckrodt

Preventing and Treating Missing Data in Longitudinal Clinical Trials (eBook, PDF)

A Practical Guide

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 28. Januar 2013
• Englisch
• ISBN-13: 9781107302938
• Artikelnr.: 38242025

Autorenporträt

Craig H. Mallinckrodt is Research Fellow in the Decision Sciences and Strategy Group at Eli Lilly and Company. Dr Mallinckrodt has supported drug development in all four clinical phases and in several therapeutic areas. He currently leads Lilly's Advanced Analytics hub for missing data and their Placebo Response Task Force, and is a member of a number of other scientific work groups. He has authored more than 170 papers, book chapters and texts, including extensive works on missing data and longitudinal data analysis in journals such as Statistics in Medicine, Pharmaceutical Statistics, the Journal of Biopharmaceutical Statistics, the Journal of Psychiatric Research, the Archives of General Psychiatry, and Nature. He currently chairs the Drug Information Association's Scientific Working Group on Missing Data.

Inhaltsangabe

Part I. Background and Setting: 1. Why missing data matter
2. Missing data mechanisms
3. Estimands
Part II. Preventing Missing Data: 4. Trial design considerations
5. Trial conduct considerations
Part III. Analytic Considerations: 6. Methods of estimation
7. Models and modeling considerations
8. Methods of dealing with missing data
Part IV. Analyses and the Analytic Road Map: 9. Analyses of incomplete data
10. MNAR analyses
11. Choosing primary estimands and analyses
12. The analytic road map
13. Analyzing incomplete categorical data
14. Example
15. Putting principles into practice.