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Additive Transformation Models for Multivariate Interval-Censored Data

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  • Pao-Sheng Shen

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.

Suggested Citation

  • Pao-Sheng Shen, 2015. "Additive Transformation Models for Multivariate Interval-Censored Data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(5), pages 1065-1079, March.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:5:p:1065-1079
    DOI: 10.1080/03610926.2012.762398
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    Cited by:

    1. Gamage, Prabhashi W. Withana & McMahan, Christopher S. & Wang, Lianming & Tu, Wanzhu, 2018. "A Gamma-frailty proportional hazards model for bivariate interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 354-366.
    2. Mengzhu Yu & Mingyue Du, 2022. "Regression Analysis of Multivariate Interval-Censored Failure Time Data under Transformation Model with Informative Censoring," Mathematics, MDPI, vol. 10(18), pages 1-17, September.

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