Regression analysis of partially linear transformed mean residual life models

Abstract

We propose a novel class of partially linear transformed mean residual life (TMRL) models to investigate linear and nonlinear covariate effects on survival outcomes of interest. A martingale-based estimating equation approach with global and kernel-weighted local estimating equations is developed to estimate the parametric and nonparametric components. Unlike the existing inverse probability of censoring weighting estimating equation approach on TMRL models, the newly proposed method avoids estimating or modeling the distribution of the censoring time, thereby enhancing model capability and computational efficiency. Furthermore, we establish the asymptotic properties for the estimators of parametric and nonparametric components and develop an efficient iterative algorithm to implement the proposed procedure. Simulation studies demonstrate the satisfactory finite sample performance of the proposed method. Finally, our model is applied to the studies of lung cancer and type 2 diabetic complications.