Statistical Modelling 9 (2009), 321–343

Linear transformation models for interval-censored data: prediction of survival probability and model checking

Zhigang Zhang
Department of Epidemiology and Biostatistics
Memorial Sloan-Kettering Cancer Center
New York
USA
eMail: zhangz@mskecc.org

Abstract:

In statistical analysis, when the value of a random variable is only known to be between two bounds, we say that this random variable is interval censored. This complicated censoring pattern is a common problem in research fields such as clinical trials or actuarial studies and raises challenges for statistical analysis. In this paper, we focus on regression analysis of case 2 interval-censored data. We first briefly review existing regression methods and an estimation approach under the class of linear transformation models developed by Zhang et al. We then propose a method for survival probability prediction via generalized estimating equations. We also consider a graphical model checking technique and a model selection tool. Some theoretical properties are established and the performance of our procedures is evaluated and illustrated by numerical studies including a real-life data analysis.

Keywords:

case 2 interval censoring; generalized estimating equation; linear transformation regression models; model checking; survival probability prediction

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