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Review of guidelines and literature for handling missing data in longitudinal clinical trials with a case study
M. Liu,
L. Wei,
J. Zhang,
Corresponding Author
M. Liu
Clinical Biostatistics, Merck & Co., Inc., Rahway, NJ, USA
Clinical Biostatistics, Merck & Co., Inc., Rahway, NJ 07065, USASearch for more papers by this authorL. Wei
Clinical Biostatistics, Merck & Co., Inc., Rahway, NJ, USA
Search for more papers by this authorJ. Zhang
Clinical Biostatistics, Merck & Co., Inc., Rahway, NJ, USA
Search for more papers by this authorM. Liu,
L. Wei,
J. Zhang,
Corresponding Author
M. Liu
Clinical Biostatistics, Merck & Co., Inc., Rahway, NJ, USA
Clinical Biostatistics, Merck & Co., Inc., Rahway, NJ 07065, USASearch for more papers by this authorL. Wei
Clinical Biostatistics, Merck & Co., Inc., Rahway, NJ, USA
Search for more papers by this authorJ. Zhang
Clinical Biostatistics, Merck & Co., Inc., Rahway, NJ, USA
Search for more papers by this authorAbstract
Missing data in clinical trials are inevitable. We highlight the ICH guidelines and CPMP points to consider on missing data. Specifically, we outline how we should consider missing data issues when designing, planning and conducting studies to minimize missing data impact. We also go beyond the coverage of the above two documents, provide a more detailed review of the basic concepts of missing data and frequently used terminologies, and examples of the typical missing data mechanism, and discuss technical details and literature for several frequently used statistical methods and associated software. Finally, we provide a case study where the principles outlined in this paper are applied to one clinical program at protocol design, data analysis plan and other stages of a clinical trial. Copyright © 2006 John Wiley & Sons, Ltd.
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