Abstract
Missing data in clinical trials are defined as planned information that was not collected. Examples of these include a subject withdrawing consent before the end of a trial or a laboratory test that cannot be obtained. Depending on why and how much data are missing, the results and interpretability of the trial can be jeopardized. Fortunately, there is a vast literature about statistical methods that can handle missing data. Probably the most important—and least technical—point in this literature is to do everything possible beginning at the trial design stage to avoid missing data. Even when the trial is designed to minimize missing data, the analysis plan should address how the analysis will proceed in the presence of missing data. This chapter should be viewed as a springboard into understanding the minimal amount of theory to apply these methods and begin reading the source literature.
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Notes
- 1.
The model can be extended further by allowing R to take on more than two values indicating multiple response patterns. This will be necessary for applying pattern mixture models.
- 2.
Common notation for the Var(e) is R but has been changed to P to avoid confusion with the response vector R.
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Gilbert, S.A., Christensen, J.C. (2018). Missing Data. In: Peace, K., Chen, DG., Menon, S. (eds) Biopharmaceutical Applied Statistics Symposium . ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7826-2_9
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DOI: https://doi.org/10.1007/978-981-10-7826-2_9
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