Randomization-Based Adjustment of Treatment Hazard Ratio for Covariates With Missing Data

ABSTRACTClinical trials are designed to evaluate treatment effects while taking into account how covariates such as age and gender may influence the comparison between treatments. Including covariates in the model to evaluate treatment effects on time to event outcomes presents complications for a regulatory clinical trial because the covariates may need to meet modeling assumptions. We provide methodology to estimate the hazard ratio for treatments in a randomized trial with time to event outcomes and missingness among the baseline covariates by adjusting for the covariates in a randomization-based way. Such adjustment for covariates is an attractive methodology in the regulatory setting as it requires only minimal assumptions. The method is illustrated for data from an oncology clinical trial. Its application is computationally straightforward for managing missing data among the baseline covariates, and its results for the illustrative clinical trial are similar to those from multiple imputation for mis...