Matching distributions for survival data

In studies with survival endpoints, it is often of interest to predict the disease risk or survival probabilities in the presence of censored failure times. One commonly used approach is to model the association between the survival outcome and covariates via a semiparametric regression model and use the fitted model for prediction. In this article, we propose two methods to evaluate or predict the survival rates. The first method estimates survival probabilities by matching survival functions, and the second one is based on matching censored quantiles. Unlike traditional regression approaches, the proposed methods directly match the distribution of linear combinations of the covariates to the entire target distribution or parts of it. To accommodate censoring, we adopt a redistribution-of-mass technique for the proposed matching censored quantiles. The asymptotic consistency of the resulting estimators is well established. Simulation studies and an example with real data are also provided to further illustrate the practical utilities of our proposals. The proposed methods have been implemented in an R package.