PARTIALLY LINEAR ADDITIVE HAZARDS REGRESSION FOR CLUSTERED AND RIGHT CENSORED DATA

Abstract

For analyzing clustered survival data, a flexible partially linear additive hazards model is proposed. To accommodate the nonlinear effects, the unknown regression function is approximated by B-splines. All regression coefficients are estimated through a system of pseudo-score functions. Under certain conditions, the proposed estimators are shown to be asymptotically normal, where a consistent estimator of the covariance matrix is given. Simulation studies are also conducted to evaluate the finite sample performance of the proposed method, which is illustrated using a real data set from an AIDS clinical trial.