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A comparison of methods to analyse continuous data from pseudo cluster randomized trials.

S Teerenstra¹, M Moerbeek, R J F Melis

¹Department of Epidemiology and Biostatistics, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands. S.Teerenstra@epib.umcn.nl

Statistics in Medicine

|March 1, 2007

Summary

Pseudo cluster randomization trials can introduce bias. Unweighted mixed models offer the best balance of feasibility and statistical power for analyzing data from these trials, outperforming other methods like GEE and paired t-tests.

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Area of Science:

Biostatistics
Clinical Trial Design
Epidemiology

Background:

Cluster randomization avoids contamination but can face recruitment challenges and bias when randomization occurs after recruitment begins.
Pseudo cluster randomization is a proposed compromise to address limitations of standard cluster randomization.
Limited understanding exists regarding the analytical methods for pseudo cluster randomization designs.

Purpose of the Study:

To compare the statistical performance of different analysis methods for pseudo cluster randomization trials.
To evaluate type I and II error rates, bias, and standard error of various estimators.
To assess the impact of weighting schemes and sample size formulas in this design.

Main Methods:

Compared mixed models, generalized estimating equations (GEE), paired t-test, and the original pseudo cluster randomization estimator.
Assessed bias in point estimates and standard errors.
Evaluated the influence of weighting schemes and sample size accuracy.

Main Results:

Unweighted mixed models and the original estimator aligned with sample size formulas for power.
Paired t-test demonstrated insufficient power.
GEE yielded inflated type I errors with fewer than 30-40 clusters per arm.
Weighting generally increased power but also type I errors for mixed models and GEE.

Conclusions:

Unweighted mixed models are recommended for analyzing pseudo cluster randomization trials.
This approach provides the optimal balance between practical implementation and statistical power.
Careful consideration of analysis methods is crucial for valid results in pseudo cluster randomization designs.