Semiparametric bayesian inference for multilevel repeated measurement data.
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.
Müller
Peter
P
Department of Biostatistics & Applied Mathematics, The University of Texas, M. D. Anderson Cancer Center, Houston, Texas 77030, USA. pmueller@mdanderson.org
Quintana
Fernando A
FA
Rosner
Gary L
GL
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D003520:Cyclophosphamide / Q000627:therapeutic use; D016178:Granulocyte-Macrophage Colony-Stimulating Factor / Q000627:therapeutic use; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000188:drug therapy; D011336:Probability; D011897:Random Allocation; D018709:Statistics, Nonparametric
D016178:Granulocyte-Macrophage Colony-Stimulating Factor; D003520:Cyclophosphamide