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.
|Publisher:||Cambridge University Press|
|Series:||Practical Guides to Biostatistics and Epidemiology Series|
|Product dimensions:||6.80(w) x 9.60(h) x 0.70(d)|
About the Author
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.
Table of Contents
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.
What People are Saying About This
"Dr. Mallinckrodt is, as usual, a paragon of clear writing and even clearer thinking. His commonsense, practical approach further elucidates what could otherwise be intractably complex issues. This book is an invaluable resource for anyone working on longitudinal clinical trials – statistician or not."
Michael Detke, MedAvante and Indiana University
"This is a timely introduction to handling missing data in clinical trials, by a statistician with wide practical experience. Sensibly, the author not only focuses on the handling of the missing data in the analysis but also explores ways in which the occurrence of missing data can be minimized through appropriate design and conduct. The book touches on the most recent developments from academia and the regulators and is presented at a level that is accessible and useful for statisticians and non-statisticians alike, and so should be both widely read and influential."
Mike Kenward, London School of Hygiene and Tropical Medicine
"Designing experiments to minimize missing data and understanding the most appropriate statistical methods to implement when analyzing a data set with missing values is of critical importance. Dr. Mallinckrodt’s text is approachable to any researcher challenged with issues surrounding missing data yet is technically comprehensive enough to be a valuable addition to any statistician’s library."
Adam Meyers, Biogen IDEC
"Dr. Mallinckrodt has worked tirelessly and successfully to promote statistically sound and practically relevant and feasible methodology to handle incomplete data from clinical trials. As an opinion leader, he is well respected by the biopharmaceutical industry, the regulatory authorities, and academe. Dr. Mallinckrodt tops off quality and relevance with an engaging and savory writing style. This highly recommended text is at the same time a page turner!"
Geert Molenberghs, Interuniversity Institute for Biostatistics and statistical Bioinformatics