Fully Bayesian hierarchical modelling in two stages, with application to meta-analysis.

ABSTRACT: Meta-analysis is often undertaken in two stages, with each study analysed separately in stage 1 and estimates combined across studies in stage 2. The study-specific estimates are assumed to arise from normal distributions with known variances equal to their corresponding estimates. In contrast, a one-stage analysis estimates all parameters simultaneously. A Bayesian one-stage approach offers additional advantages, such as the acknowledgement of uncertainty in all parameters and greater flexibility. However, there are situations when a two-stage strategy is compelling, e.g. when study-specific analyses are complex and/or time consuming. We present a novel method for fitting the full Bayesian model in two stages, hence benefiting from its advantages while retaining the convenience and

SUBMITTER: Lunn D 

PROVIDER: S-EPMC3814003 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature