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Randomization-based adjustment of multiple treatment hazard ratios for covariates with missing data
Diana Lam1,2, Gary G Koch1, John S Preisser1
1a Department of Biostatistics , University of North Carolina , Chapel Hill , North Carolina , USA.
Abstract:
Clinical trials are designed to compare treatment effects when applied to samples from the same population. Randomization is used so that the samples are not biased with respect to baseline covariates that may influence the efficacy of the treatment. We develop randomization-based covariance adjustment methodology to estimate the log hazard ratios and their confidence intervals of multiple treatments in a randomized clinical trial with time-to-event outcomes and missingness among the baseline covariates. The randomization-based covariance adjustment method is a computationally straight-forward method for handling missing baseline covariate values.
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