Understanding heterogeneity in meta‐analysis: the role of meta‐regression

Summary

Background: Meta‐regression has grown in popularity in recent years, paralleling the increasing numbers of systematic reviews and meta‐analysis published in the biomedical literature. However, many clinicians and decision‐makers may be unfamiliar with the underlying principles and assumptions made within meta‐regression leading to incorrect interpretation of their results.

Aims: This paper reviews the appropriate use and interpretation of meta‐regression in the medical literature, including cautions and caveats to its use.

Materials & Methods: A literature search of MEDLINE (OVID) from 1966‐February 2009 was conducted to identify literature relevant to the topic of heterogeneity and/or meta‐regression in systematic reviews and meta‐analysis.

Results: Meta‐analysis, a statistical method of pooling data from studies included in a systematic review, is often compromised by heterogeneity of its results. This could include clinical, methodological or statistical heterogeneity. Meta‐regression, said to be a merging of meta‐analytic and linear regression principles, is a more sophisticated tool for exploring heterogeneity. It aims to discern whether a linear relationship exists between an outcome measure and on or more covariates. The associations found in a meta‐regression should be considered hypothesis generating and not regarded as proof of causality.

Conclusions: The current review will enable clinicians and healthcare decision‐makers to appropriately interpret the results of meta‐regression when used within the constructs of a systematic review, and be able to extend it to their clinical practice.