  Ideally, a meta-analysis will summarize data from several unbiased studies.
Here we consider the less than ideal situation in which contributing studies
may be compromised by measurement error. Measurement error affects every study
design, from randomized controlled trials to retrospective observational
studies. We outline a flexible Bayesian framework for continuous outcome data
which allows one to obtain appropriate point and interval estimates with
varying degrees of prior knowledge about the magnitude of the measurement
error. We also demonstrate how, if individual-participant data (IPD) are
available, the Bayesian meta-analysis model can adjust for multiple
participant-level covariates, measured with or without measurement error.
