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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Health Services Research

Background:

  • Cluster randomized controlled trials (CRCT) are susceptible to methodological issues.
  • Impure clustering, where patients belong to multiple clusters, is a common but often unrecognized problem.
  • This can lead to significant bias and misunderstanding in trial results.

Purpose of the Study:

  • To identify and address the methodological problem of impure clustering in CRCTs.
  • To introduce the multiple membership random effects model (MMREM) as a solution.
  • To demonstrate the impact of impure clustering on statistical estimates.

Main Methods:

  • Utilized a simulated three-level cluster randomized controlled trial.
  • Modeled the data using a multiple membership random effects model (MMREM).
  • Compared results with and without accounting for multiple patient membership.

Main Results:

  • Impure clustering led to underestimation of higher-level variances.
  • Lower-level variances were overestimated when multiple membership was present.
  • Predictors at levels with multiple membership were underestimated.

Conclusions:

  • Impure clustering poses a significant threat to the validity of CRCTs.
  • The MMREM provides a robust method for handling multiple patient membership.
  • Accurate modeling is crucial to avoid biased results in cluster randomized trials.