Efficient Bayesian joint models for group randomized trials with multiple observation times and multiple outcomes.

ABSTRACT: In this paper, we propose a Bayesian method for group randomized trials with multiple observation times and multiple outcomes of different types. We jointly model these outcomes using latent multivariate normal linear regression, which allows treatment effects to change with time and accounts for (i) intraclass correlation within groups; (ii) the correlation between different outcomes measured on the same subject; and (iii) the over-time correlation of each outcome. Moreover, we develop a set of innovative priors for the variance components, which yield direct inference on the correlations, avoid undesirable constraints, and allow utilization of information from previous studies. We illustrate through simulations that our model can improve estimation efficiency (lower posterior standard de

SUBMITTER: Xu X 

PROVIDER: S-EPMC3892667 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature