Hicks, Amy, Fairhurst, Caroline orcid.org/0000-0003-0547-462X and Torgerson, David orcid.org/0000-0002-1667-4275 (2017) A simple technique investigating baseline heterogeneity helped to eliminate potential bias in meta-analyses. Journal of Clinical Epidemiology. ISSN 0895-4356
Abstract
Objectives: To perform a worked example of an approach that can be used to identify and remove potentially biased trials from metaanalyses via the analysis of baseline variables. Study Design: True randomisation produces treatment groups that differ only by chance; therefore, a meta-analysis of a baseline measurement should produce no overall difference and zero heterogeneity. A meta-analysis from the British Medical Journal, known to contain significant heterogeneity and imbalance in baseline age, was chosen. Meta-analyses of baseline variables were performed and trials systematically removed, starting with those with the largest t-statistic, until the I2 measure of heterogeneity became 0%, then the outcome meta-analysis repeated with only the remaining trials as a sensitivity check. Conclusion: We argue that heterogeneity in a meta-analysis of baseline variables should not exist, and therefore removing trials which contribute to heterogeneity from a meta-analysis will produce a more valid result. In our example none of the overall outcomes changed when studies contributing to heterogeneity were removed. We recommend routine use of this technique, using age and a second baseline variable predictive of outcome for the particular study chosen, to help eliminate potential bias in meta-analyses.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Elsevier, 2017. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Journal Article |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Health Sciences (York) |
Depositing User: | Pure (York) |
Date Deposited: | 12 Feb 2018 10:50 |
Last Modified: | 06 Dec 2023 12:16 |
Published Version: | https://doi.org/10.1016/j.jclinepi.2017.10.001 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | https://doi.org/10.1016/j.jclinepi.2017.10.001 |
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