Semiparametric Bayesian inference for multilevel repeated measurement data
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Date
2007
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Volume Title
Publisher
WILEY
Abstract
We discuss inference for data with repeated measurements at multiple levels. The motivating example is data with blood counts from cancer patients undergoing multiple cycles of chemotherapy, with days nested within cycles. Some inference questions relate to repeated measurements over days within cycle, while other questions are concerned with the dependence across cycles. When the desired inference relates to both levels of repetition, it becomes important to reflect the data structure in the model. We develop a semiparametric Bayesian modeling approach, restricting attention to two levels of repeated measurements. For the top-level longitudinal sampling model we use random effects to introduce the desired dependence across repeated measurements. We use a nonparametric prior for the random effects distribution. Inference about dependence across second-level repetition is implemented by the clustering implied in the nonparametric random effects model. Practical use of the model requires that the posterior distribution on the latent random effects be reasonably precise.
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Keywords
Bayesian nonparametrics, Dirichlet process, hierarchical model, repeated measurement data, DIRICHLET PROCESS MIXTURE, LINEAR MIXED MODELS, NONPARAMETRIC PROBLEMS, CLUSTERED DATA, REGRESSION