Regression analysis of interval-censored failure time data with possibly crossing hazards

Interval-censored failure time data occur in many areas, especially in medical follow-up studies such as clinical trials, and in consequence, many methods have been developed for the problem. However, most of the existing approaches cannot deal with the situations where the hazard functions may cross each other. To address this, we develop a sieve maximum likelihood estimation procedure with the application of the short-term and long-term hazard ratio model. In the method, the I-splines are used to approximate the underlying unknown function. An extensive simulation study was conducted for the assessment of the finite sample properties of the presented procedure and suggests that the method seems to work well for practical situations. The analysis of an motivated example is also provided.