Survival Modelling with Various Random-Effect Structures
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초록
Simple frailty models such as shared frailty have been often used for analyzing clustered time-to-event data. However, under more complex structures such as nested design or multi-center trial, the extended models are required. In this paper we review multi-component semi-parametric survival models with various random-effect structures for analyzing the complex clustered time-to-event data. For the inferences we use hierarchical likelihood (h-likelihood), and also review how to select a proper model and also to select variables of fixed effects, which are very important in regression analysis. We discuss the relative advantages of h-likelihood method compared to marginal likelihood method. The h-likelihood method is illustrated using a well-known practical data set.
Abstract
Simple frailty models such as shared frailty have been often used for analyzing clustered time-to-event data. However, under more complex structures such as nested design or multi-center trial, the extended models are required. In this paper we review multi-component semi-parametric survival models with various random-effect structures for analyzing the complex clustered time-to-event data. For the inferences we use hierarchical likelihood (h-likelihood), and also review how to select a proper model and also to select variables of fixed effects, which are very important in regression analysis. We discuss the relative advantages of h-likelihood method compared to marginal likelihood method. The h-likelihood method is illustrated using a well-known practical data set.
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