Measurement error in meta-analysis (MEMA)-A Bayesian framework for continuous outcome data subject to non-differential measurement error.

Measurement error in meta-analysis (MEMA)-A Bayesian framework for continuous outcome data subject to non-differential measurement error.

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Abstract

Ideally, a meta-analysis will summarize data from several unbiased studies. Here we look into the less than ideal situation in which contributing studies may be compromised by non-differential measurement error in the exposure variable. Specifically, we consider a meta-analysis for the association between a continuous outcome variable and one or more continuous exposure variables, where the associations may be quantified as regression coefficients of a linear regression model. A flexible Bayesian framework is developed 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, these being measured with or without measurement error.

Keywords

partial identification, misclassification, meta-analysis, measurement error, Bayesian evidence synthesis

Citation

Campbell H, de Jong VMT, Maxwell L, Jaenisch T, Debray TPA, Gustafson P. Measurement error in meta-analysis (MEMA)-A Bayesian framework for continuous outcome data subject to non-differential measurement error. Res Synth Methods. 2021 Nov;12(6):796-815. doi: 10.1002/jrsm.1515. Epub 2021 Aug 12. PMID: 34312994.