Abstract
When undertaking confirmatory analyses of data from clinical trials, statisticians frequently are confronted with having to assess and address potential biases introduced by missing data. New developments for handling missing data have proliferated in the literature. Sensitivity analysis, which allows the assessment of the impact of a wide range of nonignorable missingness mechanisms on the robustness of the statistical results, provides a reasonable alternative for analyzing trials with missing data. Two case studies utilizing sensitivity analyses in pharmaceutical industry clinical trials are presented. The first is based on an Alzheimer disease trial with a time-to-event endpoint, and the second is from an osteoporosis trial with a repeated binary outcome. The practical issues associated with the application of sensitivity analysis are discussed as well.
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Zhang, J. Sensitivity Analysis of Missing Data: Case Studies Using Model-Based Multiple Imputation. Ther Innov Regul Sci 43, 475–484 (2009). https://doi.org/10.1177/009286150904300413
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DOI: https://doi.org/10.1177/009286150904300413