Estimation of the additive hazards model with linear inequality restrictions based on current status data

The additive hazards model is one of the commonly used models for failure time data analysis and many authors have discussed its estimation under various situations. In this paper, we consider the same problem but under some inequality constraints when one faces current status data, for which it does not seem to exist an established estimation procedure due to the difficulties involved. In particular, the restricted maximum likelihood estimation approach is derived and the asymptotic properties of the resulting estimator of regression parameters are established with the use of some optimization methods. Simulation studies are conducted to assess the finite-sample performance of the proposed method and suggest that it works well for practical situations. A real data application is provided to demonstrate the utility of the proposed approach.