title: Bayesian inference for longitudinal data with non-parametric treatment effects.
abstract: We consider inference for longitudinal data based on mixed-effects models with a non-parametric Bayesian prior on the treatment effect. The proposed non-parametric Bayesian prior is a random partition model with a regression on patient-specific covariates. The main feature and motivation for the proposed model is the use of covariates with a mix of different data formats and possibly high-order interactions in the regression. The regression is not explicitly parameterized. It is implied by the random clustering of subjects. The motivating application is a study of the effect of an anticancer drug on a patient's blood pressure. The study involves blood pressure measurements taken periodically over several 24-h periods for 54 patients. The 24-h periods for each patient include a pretreatment period and several occasions after the start of therapy.
authors.affiliation: Department of Mathematics, University of Texas at Austin, Austin, TX 78712, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
authors.forename: Fernando A
authors.initials: FA
authors.lastname: Quintana
authors.forename: Gary L
authors.initials: GL
authors.lastname: Rosner
authors.forename: Michael L
authors.initials: ML
authors.lastname: Maitland
mesh_terms: D001499:Bayes Theorem; D001794:Blood Pressure / Q000187:drug effects; D001795:Blood Pressure Determination; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D013997:Time Factors; D016896:Treatment Outcome
keywords: Clustering; Mixed-effects model; Non-parametric Bayesian model; Random partition; Repeated measurement data