Survival analysis via cox proportional hazards additive models

Abstract

The Cox proportional hazards model is commonly used to examine the covariate-adjusted association between a predictor of interest and the risk of mortality for censored survival data. However, it assumes a parametric relationship between covariates and mortality risk though a linear predictor. Generalized additive models (GAMs) provide a flexible extension of the usual linear model and are capable of capturing nonlinear effects of predictors while retaining additivity between the predictor effects. In this paper, we provide a review of GAMs and incorporate bivariate additive modeling into the Cox model for censored survival data with applications to estimating geolocation effects on survival in spatial epidemiologic studies.