Semiparametric additive risks model for interval-censored data

Interval-censored event time data often arise in medical and public health studies. In such a setting, the exact time of the event of interest cannot be observed and is only known to fall between two monitoring times. Our interest focuses on the estimation of the effect of risk factors on interval-censored data under the semiparametric additive hazards model. A nonparametric step-function is used to characterize the baseline hazard function. The covariate coefficients are estimated by maximizing the observed likelihood function, and their variances are obtained using the profile likelihood approach. We show that the proposed estimates are consistent and have asymptotic normal distributions. We also show that the estimator obtained for the covariate coefficient is the most efficient estimator. Simulation studies are conducted to assess the performance of the estimate. The method is illustrated through application to a data set from an HIV study.