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Intervention effect estimates in cluster randomized versus individually randomized trials: a meta-epidemiological

Clémence Leyrat1, Agnès Caille2,3, Sandra Eldridge4

  • 1Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.

International Journal of Epidemiology
|November 13, 2018
PubMed
Summary
This summary is machine-generated.

Cluster randomized trials and individually randomized trials can be pooled in meta-analyses for binary outcomes. For continuous outcomes, further research is needed, but subgroup analyses by trial type are recommended.

Keywords:
Cluster randomized trialindividually randomized trialintervention effect estimatemeta-epidemiological studysystematic review

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Evidence Synthesis

Background:

  • Meta-analyses (MAs) frequently combine cluster randomized trials (CRTs) and individually randomized trials (IRTs).
  • Potential systematic differences in intervention effect estimates between CRTs and IRTs have not been previously investigated.
  • This meta-epidemiological study addresses this gap by comparing effect estimates from these two trial designs.

Purpose of the Study:

  • To investigate potential systematic differences in intervention effect estimates between CRTs and IRTs when pooled in meta-analyses.
  • To determine if the pooling of CRTs and IRTs in meta-analyses is appropriate for different outcome types.

Main Methods:

  • A meta-epidemiological study was conducted on Cochrane MAs published between 2010 and 2014.
  • Included MAs contained at least one CRT and one IRT.
  • Intervention effect estimates were compared using the ratio of odds ratios (ROR) for binary outcomes and the difference of standardized differences (DSMD) for continuous outcomes.

Main Results:

  • For binary outcomes, no systematic differences in effect estimates were found between CRTs and IRTs (ROR 1.00; 95% CI: 0.93 to 1.08).
  • For continuous outcomes, a statistically significant difference was observed (DSMD 0.13; 95% CI: 0.06 to 0.19), but this difference became non-significant after adjusting for trial sample size.
  • Subgroup analyses for continuous outcomes, considering pharmacological interventions or objective outcomes, did not reveal clear patterns.

Conclusions:

  • CRTs and IRTs can be pooled in meta-analyses for binary outcomes due to the absence of systematic differences in effect estimates.
  • For continuous outcomes, pooling requires caution; subgroup analyses comparing CRTs and IRTs are recommended pending further research.
  • The findings suggest that while pooling is generally safe for binary data, careful consideration and subgroup analyses are warranted for continuous data in meta-analyses.