Statistical Modelling 9 (2009), 321343
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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