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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Liu L,Ma JZ,O'Quigley Jdoi
10.1002/sim.3392subject
Has Abstractpub_date
2008-11-29 00:00:00pages
5679-91issue
27eissn
0277-6715issn
1097-0258journal_volume
27pub_type
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