R,GAM, and Survival Analysis

抄録

This article presents flexible methods for modeling censored survival data using penalized smoothing splines when the covariates values change for the duration of the study. The Cox's proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. However, a number of theoretical problems that are to be solved with respect to the baseline survival function and the baseline cumulative hazard function, are involved. The basic idea in this article is to use logistic regression model and generalized additive models with B-splines, and then estimate the survival function. The proposed methods are illustrated using data from a long-term study of patients with PBC (primary biliary cirrhosis) for the purpose of facilitating the decision as towhen to undertake liver transplantation. As illustration of graphical evaluation of covariates, the Stanford Heart Transplant data are also used which has been collected to model survival in patients. We model survival time as a function of patient covariates and transplant status, and compare the results obtained using smoothing spline, partial logistic, Cox's proportional hazards, and piecewise exponential models.