Joint analysis of multi-level repeated measures data and survival: An application to the end stage renal disease (ESRD) data

Lei Liu*, Jennie Z. Ma, John O'Quigley

*Corresponding author for this work

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

Shared random effects models have been increasingly common in the joint analyses of repeated measures (e.g. CD4 counts, hemoglobin levels) and a correlated failure time such as death. In this paper we study several shared random effects models in the multi-level repeated measures data setting with dependent failure times. Distinct random effects are used to characterize heterogeneity in repeated measures at different levels. The hazard of death may be dependent on random effects from various levels. To simplify the estimation procedure, we adopt the Gaussian quadrature technique with a piecewise log-linear baseline hazard for the death process, which can be conveniently implemented in the freely available software aML. As an example, we analyze repeated measures of hematocrit level and survival for end stage renal disease patients clustered within a randomly selected 126 dialysis centers in the U.S. renal data system data set. Our model is very comprehensive yet easy to implement, making it appealing to general statistical practitioners.

Original languageEnglish (US)
Pages (from-to)5679-5691
Number of pages13
JournalStatistics in Medicine
Volume27
Issue number27
DOIs
StatePublished - Nov 1 2008

Keywords

  • Counting process
  • Dependent censoring
  • Frailty model
  • Hierarchical model
  • Informative censoring
  • Proportional hazards model
  • Survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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