Multilevel models for survival analysis with random effects

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)96-102
Journal / PublicationBiometrics
Volume57
Issue number1
Publication statusPublished - 2001

Abstract

A method for modeling survival data with multilevel clustering is described. The Cox partial likelihood is incorporated into the generalized linear mixed model (GLMM) methodology. Parameter estimation is achieved by maximizing a log likelihood analogous to the likelihood associated with the best linear unbiased prediction (BLUP) at the initial step of estimation and is extended to obtain residual maximum likelihood (REML) estimators of the variance component. Estimating equations for a three-level hierarchical survival model are developed in detail, and such a model is applied to analyze a set of chronic granulomatous disease (CGD) data on recurrent infections as an illustration with both hospital and patient effects being considered as random. Only the latter gives a significant contribution. A simulation study is carried out to evaluate the performance of the REML estimators. Further extension of the estimation procedure to models with an arbitrary number of levels is also discussed.

Research Area(s)

  • Chronic granulomatous disease, Generalized linear mixed model, Multilevel model, Random effects, Residual maximum likelihood, Survival analysis

Citation Format(s)

Multilevel models for survival analysis with random effects. / Yau, K. K W.

In: Biometrics, Vol. 57, No. 1, 2001, p. 96-102.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review