Distributional Imputation for Control-Based Sensitivity Analyses of Recurrent Events Data

Oregon Convention Center 

Longitudinal clinical trials for which recurrent events endpoints are of interest are commonly subject to missing event data. Primary analyses in such trials are often performed assuming events are missing at random. Control-based imputation methods are advantageous for performing necessary sensitivity analyses in superiority trials to assess robustness of primary analysis conclusions to missing data assumptions. Multiple imputation (MI) is a popular approach for control-based imputation of recurrent events, but Rubin's variance estimator is often biased for the true sampling variability of the treatment effect estimator in the control-based setting. The nonparametric bootstrap is a common approach to overcome this issue, but can be computationally intensive. We propose distributional imputation (DI) with a corresponding wild bootstrap variance estimation procedure for control-based sensitivity analyses of recurrent events. In simulations, DI produced more reasonable standard error estimates than MI with the standard variance estimator and provided gains in computational efficiency over MI with a nonparametric bootstrap.

recurrent events

missing data

control-based imputation

distributional imputation

multiple imputation

sensitivity analyses 

Biopharmaceutical Section