Additive Transformation Models for Multivariate Interval-Censored Data

Abstract

Tong, et al. (2008) considered multivariate (clustered) interval-censored failure time data that occur when there exist several correlated survival times of interest and only interval-censored data are available for each survival time. Assuming that covariates affect the hazard rate linearly, they developed a marginal inference approach using the additive hazards model. In this article, based on the idea of Zeng and Cai (2010), we consider a general class of additive transformation model, which relaxes the linear assumption. Using working independence likelihood, we present an inference approach for regression analysis of multivariate interval-censored data. A simulation study is conducted to investigate the performance of the proposed estimator. We apply the proposed method to the data set from the Diabetic Retinopathy Study.