Semiparametric regression with the U-shaped baseline hazard function in the additive hazards model under general censoring mechanisms

Abstract

When employing natural shape constraint knowledge obtained from prior studies in survival data analysis, both restrictions of parametric models and difficulties of nonparametric approaches, including the selection of bandwidth and tuning parameter, can be avoided. We use this technique to derive a nonparametric estimator of the U-shaped baseline hazard function in the semiparametric additive hazards regression model for partly general interval-censored data. An iterative algorithm is developed for computing estimators of the baseline hazard function and regression coefficients simultaneously. Numerical studies using simulation and real dataset are conducted to compare the performance of the proposed approach incorporating shape information with that of the B-spline and I-spline methods without using shape restriction. They show that the proposed method increases the efficiency of estimator for the baseline hazard function in terms of the mean Hellinger distance and performs better, or at least leads to competitive results for estimating regression coefficients.