Focuses on the prevention and treatment of missing data in longitudinal clinical trials, looking at key principles and explaining analytic methods.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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