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Related Experiment Video

Updated: Mar 14, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Choosing appropriate analysis methods for cluster randomised cross-over trials with a binary outcome.

Katy E Morgan¹, Andrew B Forbes², Ruth H Keogh¹

¹Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.

Statistics in Medicine

|September 30, 2016

Summary

This summary is machine-generated.

Cluster randomised cross-over (CRXO) trials require accounting for within-cluster and within-period correlations. An unweighted cluster-level summary regression best maintained correct Type I error rates in simulations.

Keywords:

binary outcomes cluster randomised cross-over intra-cluster correlation coefficient (ICC)randomised trial

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Area of Science:

Biostatistics
Clinical Trials Methodology
Epidemiology

Background:

Cluster randomised cross-over (CRXO) trials involve multiple treatments assigned sequentially within clusters.
Standard analyses may overlook correlations between patients within the same cluster and period.
Accurate statistical analysis is crucial for reliable trial outcomes.

Purpose of the Study:

To evaluate analytical methods for binary outcomes in two-period CRXO trials.
To assess the impact of within-cluster and within-period correlations on Type I error rates.
To identify robust statistical approaches for CRXO trial data.

Main Methods:

Simulation study comparing various statistical models for binary outcomes.
Analysis included hierarchical models with and without specific random effects.
Generalized estimating equations and cluster-level summary regression were also evaluated.

Main Results:

Hierarchical models lacking period-within-cluster random effects showed inflated Type I errors.
Models with period-within-cluster random effects had correct Type I errors only with numerous clusters.
Generalized estimating equations failed to maintain correct error rates.
Unweighted cluster-level summary regression performed best overall, but with reduced power in some scenarios.

Conclusions:

Accurate analysis of CRXO trials necessitates modeling both cluster and period-within-cluster correlations.
Failure to account for within-period correlation can lead to inflated Type I errors.
Unweighted cluster-level summary regression offers a robust approach, though power considerations are important.