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Preventing and Treating Missing Data in Longitudinal Clinical Trials
A Practical Guide


Part of Practical Guides to Biostatistics and Epidemiology

  • Date Published: March 2013
  • availability: In stock
  • format: Paperback
  • isbn: 9781107679153

£ 28.99

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About the Authors
  • 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.

    • Brings a practical approach to both the analysis of incomplete data and most importantly to preventing or limiting the amount of missing data
    • Statistical notation and jargon is limited making it accessible to non-statistical audiences
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    Reviews & endorsements

    '… this monograph is good value, and I recommend all those involved in the design, conduct or analysis of trials to peruse a copy. Non-statisticians will inevitably be frustrated at times, but if this monograph fosters improved discussion and understanding of the issues raised by missing data in study teams - and how they might be addressed - it will have done its work. In his choice of audience Mallinckrodt set himself a high bar … it has … been cleared. In addition, [his] wry turn of phrase was an unexpected pleasure. You'll miss this if you don't buy it!' James R. Carpenter, Journal of Biopharmaceutical Statistics

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    Product details

    • Date Published: March 2013
    • format: Paperback
    • isbn: 9781107679153
    • length: 180 pages
    • dimensions: 247 x 174 x 11 mm
    • weight: 0.36kg
    • contains: 6 b/w illus. 32 tables
    • availability: In stock
  • 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.

  • Author

    Craig H. Mallinckrodt, Eli Lilly and Company, Indianapolis, IN
    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.

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