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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.

SUBMITTER: Muller P

PROVIDER: S-EPMC3944972 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Bayesian inference for longitudinal data with non-parametric treatment effects.

Müller Peter P Quintana Fernando A FA Rosner Gary L GL Maitland Michael L ML

Biostatistics (Oxford, England) 20131126 2

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 r ...[more]

PMID: 24285773

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