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Performance of methods for analyzing continuous data from stratified cluster randomized trials - A simulation study.

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This study compared analysis methods for stratified cluster randomized trials (CRTs). Meta-regression showed lower efficiency and higher type I error rates, especially with fewer clusters, compared to other methods.

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Stratified cluster randomized trials (CRTs) are increasingly utilized in research.
  • This design involves grouping clusters into strata before randomization to treatment groups.
  • Accurate analysis of continuous data from stratified CRTs is crucial.

Purpose of the Study:

  • To evaluate the performance of common statistical methods for analyzing continuous data from stratified CRTs.
  • To compare mixed-effects, generalized estimating equation (GEE), cluster-level (CL) linear regression, and meta-regression methods.
  • To assess performance based on type I error rate, power, accuracy (RMSE), and confidence interval characteristics.

Main Methods:

  • A simulation study was conducted using a stratified CRT design with one stratification variable and two strata.
  • Simulations varied the number of clusters, cluster sizes, intra-cluster correlation coefficients (ICCs), and effect sizes.
  • Four analysis methods were compared: mixed-effects, GEE, CL linear regression, and meta-regression.

Main Results:

  • Generalized estimating equation (GEE) and meta-regression methods exhibited high type I error rates (>10%) with a small number of clusters.
  • All methods demonstrated similar accuracy (RMSE) and 95% confidence interval (CI) widths, except for meta-regression, particularly with fewer clusters.
  • Empirical power decreased across all methods as the intra-cluster correlation coefficient (ICC) increased for a given sample size.

Conclusions:

  • Meta-regression was found to be the least efficient method for analyzing continuous data from stratified CRTs.
  • The choice of analysis method impacts the reliability of results, especially concerning type I error rates in small cluster settings.
  • Further research may be needed to refine methods for stratified CRT data analysis.