Maximum likelihood estimation in proportional odds regression model based on interval-censored event-time data

Maximum likelihood estimates of density function and regression coefficients in the proportional odds regression models are proposed and studied based on event-time data that are either completely or partly interval-censored. A smooth estimate of the survival function is then obtained. Theoretical results indicate that the proposed method enjoys an almost parametric $\sqrt{n}$-consistency. Some simulation studies show that the proposed method not only gives density and smooth survival curve estimates but also outperforms the existing semiparametric method in terms of estimating both the regression coefficients and the survival curves for small and medium sample sizes. The proposed method is illustrated by an application to the HIV infection data.