Estimation of regression parameters and the hazard function in transformedlinear survival models

An estimator of the regression parameters in a semiparametric transformed l inear survival model is examined. This estimator consists of a single Newto n-like update of the solution to a rank-based estimating equation from an i nitial consistent estimator. An automated penalized likelihood algorithm is proposed for estimating the optimal weight function for the estimating equ ations and the error hazard function that is needed in the variance estimat or. In simulations, the estimated optimal weights are found to give reasona bly efficient estimators of the regression parameters, and the variance est imators are found to perform well. The methodology is applied to an analysi s of prognostic factors in non-Hodgkin's lymphoma.