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Abstract:
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Sensitivity analyses using multiple imputation (MI) provide a flexible approach to assess the impact of missing data on clinical trial results. An approach which imputes missing data in the test drug group using a model built from the control group has gained attention in recent research. This control-based imputation (CBI) approach typically provides a conservative point estimate for treatment difference. A standard multiple imputation approach with Rubin's rule is commonly used to implement this method. However, the combined variance from Rubin's rule may over-estimate the variability. In this talk, we show an alternative implementation using a delta-adjustment approach for tipping point analysis which helps us to understand and interpret the results from CBI using MI method with Rubin's rule. In addition, we propose a new sensitivity measure for assessing the robustness of the result obtained under a missing at random (MAR) assumption. Results from simulation studies and applications to a longitudinal clinical trial dataset are presented for illustration.
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