Multilevel models for survival analysis with random effects.
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.
Department of Management Sciences, City University of Hong Kong, Kowloon. mskyau@cityu.edu.hk
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D001699:Biometry; D016000:Cluster Analysis; D003198:Computer Simulation; D006105:Granulomatous Disease, Chronic / Q000188:drug therapy; D006801:Humans; D017053:Infection Control; D007371:Interferon-gamma / Q000627:therapeutic use; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D016016:Proportional Hazards Models; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012008:Recurrence; D016019:Survival Analysis
D007371:Interferon-gamma