Semiparametric Regression Analysis of Survival and Longitudinal Data

Abstract

The proportional odds model is used in survival analysis when the odds ratios over time are constant. The assumption, however, is often violated in many applications. We propose a novel semiparametric general odds ratio model for the analysis of right-censored survival data. The proposed model incorporates the short-term and long-term covariate effects on the failure time data, and includes the proportional odds model as a special case. We derive efficient likelihood-based inference procedures and establish the large sample properties of the proposed nonparametric maximum likelihood estimators. Extensive simulation studies demonstrate the proposed methods perform well in practical settings. An application to a breast cancer study is provided.