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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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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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Estimands in cluster-randomized trials: choosing analyses that answer the right question.

Brennan C Kahan1, Fan Li2,3, Andrew J Copas1

  • 1MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, London, UK.

International Journal of Epidemiology
|July 14, 2022
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Summary

Cluster-randomized trials (CRTs) can estimate different treatment effects. Choosing the correct analysis ensures unbiased results, especially when cluster size influences outcomes, leading to clearer study interpretations.

Keywords:
Participant-average treatment effectcluster-average treatment effectcluster-randomized trialestimandsindependence estimating equationsinformative cluster size

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Cluster-randomized trials (CRTs) randomize groups to interventions.
  • Analysis methods for CRTs exist, but estimand choices are often underexplored.
  • Understanding estimands is crucial for interpreting CRT results.

Purpose of the Study:

  • To describe different estimands addressable by CRTs.
  • To demonstrate how analytic choices impact interpretation by altering the target estimand.
  • To highlight the importance of aligning analysis with the research question.

Main Methods:

  • Distinguishing between participant-average and cluster-average treatment effects.
  • Identifying conditions where cluster size is informative and affects estimands.
  • Evaluating the bias of common estimators (mixed-effects models, GEE) under informative cluster size.
  • Proposing alternative unbiased estimators (independence estimating equations, cluster-level analyses).

Main Results:

  • CRTs can target participant-average or cluster-average treatment effects.
  • Estimands differ when cluster size is informative (e.g., due to staffing or participant types).
  • Common estimators can be biased with informative cluster sizes; alternative methods offer unbiased estimates.

Conclusions:

  • Careful estimand specification is essential at the study's outset.
  • Clear estimands ensure the research question is well-defined and relevant.
  • Appropriate estimator selection guarantees an unbiased estimate of the desired treatment effect.