  We study explained variation under the additive hazards regression model for
right-censored data. We consider different approaches for developing such a
measure, and focus on one that estimates the proportion of variation in the
failure time explained by the covariates. We study the properties of the
measure both analytically, and through extensive simulations. We apply the
measure to a well-known survival data set as well as the linked Surveillance,
Epidemiology and End Results (SEER)-Medicare database for prediction of
mortality in early-stage prostate cancer patients using high dimensional claims
codes.
