Amy Herring, Harvard University
Missing Covariates in Survival Analysis
Problems associated
with missing covariate data are well-known but often ignored.
Intuitively, when subjects with missing covariate values differ
systematically from those with complete data with respect to the
outcome of interest, results from a traditional complete case data
analysis omitting the missing cases may no longer be valid. We
develop methods for handling missing covariates in the framework of
the Cox regression model. We propose a model-based procedure with a
Monte Carlo EM algorithm that allows us to make inferences based on
the likelihood of the observed data. The estimation procedure is
illustrated using cancer clinical trials in which categorical and
continuous covariates are not completely observed. This methodology
may be applied when covariates are ignorably or nonignorably missing
and has been extended to allow the inclusion of random
effects.