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Appropriate statistical methods for analysing partially nested randomised controlled trials with continuous outcomes:

Jane Candlish1, M Dawn Teare2, Munyaradzi Dimairo2

  • 1School of Health and Related Research (ScHARR), University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK. jane.candlish@sheffield.ac.uk.

BMC Medical Research Methodology
|October 14, 2018
PubMed
Summary
This summary is machine-generated.

Partially nested randomized controlled trials (pnRCTs) require careful analysis to account for clustering. A heteroscedastic partially nested mixed-effects model is recommended for continuous outcomes, ensuring accurate statistical inference.

Keywords:
ClusteringIndividually randomised cluster trialIndividually randomised group treatmentIntervention studiesPartially clusteredPartially nestedRandomised controlled trialTherapist effects

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Clustering of outcomes can occur in individually randomized trials, particularly when interventions are group-delivered.
  • Partially nested randomized controlled trials (pnRCTs) exhibit clustering in only one arm, typically the intervention arm.
  • Accurate measurement and analysis of between-cluster variability are crucial for pnRCTs.

Purpose of the Study:

  • To compare various analysis approaches for pnRCTs with continuous outcomes.
  • To investigate the impact of cluster size, control arm coding, intracluster correlation coefficient (ICC), and differential variance on statistical inference.
  • To provide recommendations for the analysis of pnRCTs.

Main Methods:

  • A simulation study was conducted to evaluate six analysis methods for a two-arm pnRCT with continuous outcomes.
  • Methods included linear regression, various fully clustered mixed-effects models, and partially nested mixed-effects models (homoscedastic and heteroscedastic).
  • Simulations varied cluster size, number of clusters, ICC, and individual variance between arms.

Main Results:

  • All models yielded unbiased intervention effect estimates.
  • Failure to account for ICCs inflated Type I error rates and confidence interval coverage.
  • Fully clustered models showed poor Type I error control and biased ICC estimates.
  • The heteroscedastic partially nested mixed-effects model demonstrated good Type I error control and unbiased ICC estimation.

Conclusions:

  • The heteroscedastic partially nested mixed-effects model is generally recommended for continuous outcomes in pnRCTs.
  • This model effectively handles clustering in one arm.
  • In specific scenarios with few clusters, small cluster sizes, and small ICCs, this model may underestimate Type I error rates, indicating no single optimal model.