Multilevel Mixed Linear Models for Survival Data

Authors: Il Do Ha¹; Youngjo Lee²

Source: Lifetime Data Analysis, Volume 11, Number 1, March 2005 , pp. 131-142(12)

Publisher: Springer

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Abstract:

For the analysis of correlated survival data mixed linear models are useful alternatives to frailty models. By their use the survival times can be directly modelled, so that the interpretation of the fixed and random effects is straightforward. However, because of intractable integration involved with the use of marginal likelihood the class of models in use has been severely restricted. Such a difficulty can be avoided by using hierarchical-likelihood, which provides a statistically efficient and fast fitting algorithm for multilevel models. The proposed method is illustrated using the chronic granulomatous disease data. A simulation study is carried out to evaluate the performance.

Keywords: hierarchical-likelihood; multilevel frailty models; multilevel mixed linear models; nested random effects

Document Type: Research article

DOI: http://dx.doi.org/10.1007/s10985-004-5644-2

Affiliations: 1: Faculty of Information Science, Daegu Haany University, 712-240, Kyungsan, South Korea, Email: idha@dhu.ac.kr 2: Department of Statistics, Seoul National University, 151-742, Seoul, South Korea, Email: youngjo@plaza.snu.ac.kr

Publication date: 2005-03-01