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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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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Demystifying estimands in cluster-randomised trials.

Brennan C Kahan1, Bryan S Blette2, Michael O Harhay1,3

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

Statistical Methods in Medical Research
|May 23, 2024
PubMed
Summary
This summary is machine-generated.

Carefully defining estimands and choosing appropriate estimators are crucial for interpreting treatment effects in cluster-randomised trials. This ensures the study addresses the intended research question accurately.

Keywords:
Estimandanalysis of cluster-level summariescluster-averagecluster-randomised trialcluster-specificindependence estimating equationsmarginalparticipant-average

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology

Background:

  • Estimands clarify treatment effect interpretation and align estimators with study objectives.
  • Cluster-randomised trials (CRTs) require specific attributes for estimands, including marginal vs. cluster-specific and participant-level vs. cluster-level average effects.

Purpose of the Study:

  • To provide formal definitions of estimands for CRTs using potential outcomes notation.
  • To describe differences between various estimand types in CRTs.
  • To present estimators for each estimand, detailing their assumptions and consistency.

Main Methods:

  • Formal definitions of estimands using potential outcomes notation.
  • Overview of estimators, their assumptions, and consistency proofs.
  • Re-analysis of a published cluster-randomised trial.

Main Results:

  • The choice of estimand and estimator significantly impacts the interpretation of treatment effects in CRTs.
  • Estimated odds ratios varied from 1.38 to 1.83 based on the target estimand.
  • In some cases, estimator choice influenced conclusions, leading to smaller treatment effect estimates.

Conclusions:

  • Precise specification of the estimand is essential for CRTs.
  • Appropriate selection of estimators is critical for valid interpretation of CRT results.
  • Careful estimand and estimator specification ensures CRTs answer the intended research question.