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Analysis of a cluster randomized trial with binary outcome data using a multi-level model.

R Z Omar1, S G Thompson

  • 1Department of Epidemiology and Public Health, Imperial College of Science, Technology and Medicine, Du Cane Road, London W12 ONN, U.K. r.omar@ic.ac.uk

Statistics in Medicine
|September 15, 2000
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Summary
This summary is machine-generated.

Multi-level logistic regression models effectively analyze cluster randomized trial data, accounting for between-practice variability. This approach offers more reliable estimates of intervention effects compared to other statistical methods.

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Area of Science:

  • Biostatistics
  • Public Health Research
  • Health Services Research

Background:

  • Cluster randomized trials (CRTs) are essential for evaluating health interventions in real-world settings.
  • Analyzing CRT data requires methods that account for the hierarchical structure, such as women nested within general practices.
  • Previous analyses of such data have used various statistical approaches with differing results.

Purpose of the Study:

  • To explore the utility of multi-level logistic regression models for analyzing data from a cluster randomized trial.
  • To compare multi-level models with fixed effect (FE) and random effects (RE) cluster summary statistic methods, ordinary logistic regression, and generalized estimating equations (GEE).
  • To assess the accuracy of variance estimation and statistical significance of the intervention effect across different models.

Main Methods:

  • A two-level logistic regression model was employed to analyze data from a CRT involving 26 general practices.
  • Comparisons were made with FE and RE cluster summary statistic methods, ordinary logistic regression, and a marginal model using GEE.
  • Parametric bootstrap methods were utilized within the multi-level model framework.

Main Results:

  • FE methods and ordinary logistic regression understated variance, overstating statistical significance.
  • The marginal model (GEE) showed higher statistical significance than RE summary statistics and multi-level models.
  • Multi-level models and marginal models accommodate multiple covariates, unlike RE summary statistics.
  • Multi-level models provide direct estimates of variance components, which are crucial for assessing variability.

Conclusions:

  • Multi-level logistic regression models are suitable for analyzing CRT data with nested structures, providing accurate variance estimates.
  • These models offer advantages over FE/RE summary statistics and GEE, particularly when variance components are of interest.
  • The multi-level framework allows for checking assumptions and extending analyses to multiple sources of variability, enhancing the reliability of intervention effect estimates.