Abstract
Expectile regression can be used to analyze the entire conditional distribution of a response, omitting all distributional assumptions. Among its benefits are computational simplicity, efficiency, and the possibility to incorporate a semiparametric predictor. Due to its advantages in full data settings, we propose an extension to right-censored data situations, where conventional methods typically focus only on mean effects. We propose to extend expectile regression with inverse probability weights. Estimates are easy to implement and computationally simple. Expectiles can be converted to more easily interpreted tail expectations, that is, the expected residual life. It provides a meaningful effect measure, similar to the hazard rate. The results from an extensive simulation study are presented, evaluating consistency and sensitivity to violations of assumptions. We use the proposed method to analyze survival times of colorectal cancer patients from a regional certified high volume cancer center.
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