An Improved Survival Estimator for Medical Costs using Kernel

Abstract

Costs assessment and cost-effectiveness analysis serve as an essential part in economic evaluation of medical interventions. In clinical trials and many observational studies, costs as well as survival data are frequently censored. Standard techniques for survival-type data are often invalid in analyzing censored cost data, due to the induced dependent censoring problem (Lin et al., 1997). In this talk, we will first examine an equivalency between a redistribute-to-the right (RR) algorithm and the popular Kaplan-Meier method for estimating the survival function of time (Efron, 1967). Next, we will extend the RR algorithm to the problem of estimating the survival function of medical costs, and discuss RR-based estimators. Finally, we will propose a kernel-based estimator for the survival function of costs which will further improve the RR estimators. We will conduct simulation experiments to compare these survival estimators for costs and apply them to a data example from a randomized cardiovascular clinical trial.?