Abstract
Case-1 and case-2 interval-censored failure time data commonly occur in medical research as well as other fields and many methods have been developed for their analysis under different frameworks. In this paper, we consider regression analysis of such data and present a general class of nonparametric transformation models. One major advantage of these models is their flexibility and generality as they include the linear transformation model as a special case. For estimation of regression parameters, we propose a weighted rank (WR) estimation procedure and establish the consistency and asymptotic normality of the resulting estimator. Furthermore, to estimate the asymptotic covariance matrix of the proposed estimator, a resampling technique, which does not involve nonparametric density estimation or numerical derivatives, is developed. A numerical study is also conducted and suggests that the proposed methodology works well in practice. Finally an application is provided.
Acknowledgments
The authors thank two anonymous referees and the associate editor for their careful reading of the paper and insightful comments. On behalf of all authors, the corresponding author states that there is no conflict of interest.
Disclosure statement
No potential conflict of interest was reported by the author(s).