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Multilevel models for survival analysis with random effects.

K K Yau1

  • 1Department of Management Sciences, City University of Hong Kong, Kowloon. mskyau@cityu.edu.hk

Biometrics
|March 17, 2001
PubMed
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This study introduces a novel method for survival data analysis using generalized linear mixed models (GLMMs), enhancing multilevel clustering insights. The approach effectively models complex hierarchical data, offering robust variance component estimation for survival outcomes.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Survival Analysis

Background:

  • Multilevel clustering in survival data presents analytical challenges.
  • Generalized linear mixed models (GLMMs) offer a flexible framework for correlated data.
  • Accurate estimation of variance components is crucial for understanding hierarchical effects.

Purpose of the Study:

  • To develop and present a method for modeling survival data with multilevel clustering.
  • To incorporate Cox partial likelihood within the GLMM framework.
  • To detail estimation procedures for three-level hierarchical survival models.

Main Methods:

  • Utilized generalized linear mixed model (GLMM) methodology.
  • Incorporated Cox partial likelihood for survival data.

Related Experiment Videos

  • Employed a two-step estimation process involving BLUP-analogous likelihood maximization and REML for variance components.

    • Main Results:

    • Developed detailed estimating equations for a three-level hierarchical survival model.
    • Applied the model to chronic granulomatous disease (CGD) data, identifying significant patient-level random effects.
    • Simulation studies validated the performance of REML estimators.

    Conclusions:

    • The proposed GLMM-based method effectively models survival data with multilevel structures.
    • Patient-level random effects were significant in the CGD recurrent infection dataset.
    • The methodology is extendable to survival models with an arbitrary number of hierarchical levels.