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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Informative cluster size in cluster-randomised trials: A case study from the TRIGGER trial.

Brennan C Kahan1, Fan Li2, Bryan Blette3

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Summary

Cluster-randomised trials may yield biased results if cluster size influences outcomes. Independence estimating equations offer unbiased estimates for participant-average and cluster-average treatment effects when cluster size is informative.

Keywords:
Cluster-randomised trialcluster-average treatment effectestimandinformative cluster sizeparticipant-average treatment effect

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Cluster-randomised trials (CRTs) can estimate distinct participant-average and cluster-average treatment effects.
  • These effects can diverge when cluster size is informative, potentially biasing standard estimators.
  • Little empirical research exists on the prevalence of informative cluster size in CRTs.

Purpose of the Study:

  • To empirically investigate informative cluster size in CRTs by re-analysing a trial on red blood cell transfusion thresholds.
  • To compare participant-average and cluster-average treatment effect estimates.
  • To evaluate the performance of different statistical methods under potential informative cluster size.

Main Methods:

  • Re-analysis of a CRT comparing transfusion thresholds for acute upper gastrointestinal bleeding.
  • Estimation of participant-average effects using independence estimating equations (unbiased under informative cluster size).
  • Comparison with cluster-average effect estimators (weighted independence estimating equations, unweighted cluster-level summaries) and mixed-effects models/GEE with exchangeable correlation.

Main Results:

  • Estimates for participant- and cluster-average effects differed by >10% for 29% of outcomes.
  • Notable discrepancies were observed between independence estimating equations and mixed-effects models/GEE.
  • Specific examples include EQ-5D VAS scores and thromboembolic/ischaemic events, highlighting potential bias.

Conclusions:

  • Estimates in CRTs can differ due to informative cluster size, necessitating careful method selection.
  • Independence estimating equations and weighted cluster-level summaries are preferred when informative cluster size is plausible.
  • Appropriate statistical methods ensure unbiased estimation of treatment effects in CRTs.