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Exploring treatment by covariate interactions using subgroup analysis and meta-regression in cochrane reviews: a
Sarah Donegan1, Lisa Williams1, Sofia Dias2
1Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom.
Background:
Treatment by covariate interactions can be explored in reviews using interaction analyses (e.g., subgroup analysis). Such analyses can provide information on how the covariate modifies the treatment effect and is an important methodological approach for personalising medicine. Guidance exists regarding how to apply such analyses but little is known about whether authors follow the guidance.
Methods:
Using published recommendations, we developed criteria to assess how well interaction analyses were designed, applied, interpreted, and reported. The Cochrane Database of Systematic Reviews was searched (8th August 2013). We applied the criteria to the most recently published review, with an accessible protocol, for each Cochrane Review Group. We excluded review updates, diagnostic test accuracy reviews, withdrawn reviews, and overviews of reviews. Data were summarised regarding reviews, covariates, and analyses.
Results:
Each of the 52 included reviews planned or did interaction analyses; 51 reviews (98%) planned analyses and 33 reviews (63%) applied analyses. The type of analysis planned and the type subsequently applied (e.g., sensitivity or subgroup analysis) was discrepant in 24 reviews (46%). No review reported how or why each covariate had been chosen; 22 reviews (42%) did state each covariate a priori in the protocol but no review identified each post-hoc covariate as such. Eleven reviews (21%) mentioned five covariates or less. One review reported planning to use a method to detect interactions (i.e., interaction test) for each covariate; another review reported applying the method for each covariate. Regarding interpretation, only one review reported whether an interaction was detected for each covariate and no review discussed the importance, or plausibility, of the results, or the possibility of confounding for each covariate.
Conclusions:
Interaction analyses in Cochrane Reviews can be substantially improved. The proposed criteria can be used to help guide the reporting and conduct of analyses.
Insights
Interaction analyses in systematic reviews are crucial for personalized medicine but often poorly reported. Authors need to improve the design, application, interpretation, and reporting of these important analyses.
Area of Science:
- Medical research methodology
- Evidence synthesis
- Clinical trial analysis
Background:
- Interaction analyses, such as subgroup analyses, are vital for understanding how covariates modify treatment effects in systematic reviews.
- These analyses are key to the methodological approach for personalizing medicine.
- Existing guidance on conducting interaction analyses is available, but adherence by authors is not well understood.
Purpose of the Study:
- To develop and apply criteria for assessing the quality of interaction analyses in systematic reviews.
- To evaluate the design, application, interpretation, and reporting of interaction analyses based on established recommendations.
Main Methods:
- Criteria were developed based on published recommendations for assessing interaction analyses.
- The Cochrane Database of Systematic Reviews was searched, and the criteria were applied to the most recent eligible review from each Cochrane Review Group.
- Exclusions included review updates, diagnostic test accuracy reviews, withdrawn reviews, and overviews of reviews.
Main Results:
- All 52 included reviews planned or conducted interaction analyses; 98% planned them, and 63% applied them.
- Discrepancies between planned and applied analyses occurred in 46% of reviews.
- Reporting of covariate selection, a priori/post-hoc identification, and interpretation of interaction results was notably lacking across reviews.
Conclusions:
- Significant improvements are needed in the conduct and reporting of interaction analyses within Cochrane Reviews.
- The developed criteria offer a framework to guide authors in conducting and reporting these analyses more effectively.
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