Semiparametric odds rate model for modeling short-term and long-term effects with application to a breast cancer genetic study.

The proportional odds model is commonly used in the analysis of failure time data. The assumption of constant odds ratios over time in the proportional odds model, however, can be violated in some applications. Motivated by a genetic study with breast cancer patients, we propose a novel semiparametric odds rate 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 nested model. We develop efficient likelihood-based inference procedures and establish the large sample properties of the proposed nonparametric maximum likelihood estimators. Simulation studies demonstrate that the proposed methods perform well in practical settings. An application to the motivating example is provided.