Preventing and treating missing data in longitudinal clinical trials : a practical guide / / Craig H. Mallinckrodt.
"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...
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| Year of Publication: | 2013 |
| Language: | English |
| Series: | Practical guides to biostatistics and epidemiology
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| Physical Description: | xviii, 165 p. :; ill. |
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Table of Contents:
- Machine generated contents note: 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.