Additive Transformation Models for Multivariate Interval-Censored Data

Pao-Sheng Shen

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 5, 1065-1079

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.

Date: 2015
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Citations: View citations in EconPapers (2)

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DOI: 10.1080/03610926.2012.762398

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