Sensitivity Analysis for Missing Outcomes in Time-to-event Data with Covariate Adjustment - Zhao, Yue (J Biopharm Stat, 2015)
Covariate-adjusted sensitivity analyses is proposed for missing time-to-event outcomes. The method invokes multiple imputation (MI) for the missing failure times under a variety of specifications regarding the post-withdrawal tendency for having the event of interest. With a clinical trial example, we compared methods of covariance analyses for time-to-event data, i.e., the multivariable Cox proportional hazards model and non-parametric ANCOVA, and then illustrated how to incorporate these methods into the proposed sensitivity analysis for covariate adjustment. The MI methods considered are Kaplan-Meier Multiple Imputation (KMMI), covariate-adjusted and unadjusted proportional hazards multiple imputation (PHMI). The assumptions, statistical issues, and features for these methods are discussed.