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A comparative study of R functions for clustered data analysis.

Wei Wang1, Michael O Harhay2,3

  • 1Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. wei.wang@pennmedicine.upenn.edu.

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Summary
This summary is machine-generated.

This study compared statistical methods for analyzing clustered medical data. The "gls" function efficiently estimates intra-class correlation, but confidence intervals may not be obtainable with high within-group correlation.

Keywords:
Cluster randomized trialsClustered data analysisGeneralized estimating equationsMixed effects models

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Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Statistical Modeling

Background:

  • Clustered or correlated outcome data is prevalent in medical research, including disease registries and cluster-randomized trials.
  • Accounting for within-group correlation using methods like generalized estimating equations and mixed-effects models is crucial for unbiased statistical inference.

Purpose of the Study:

  • To compare different approaches for estimating generalized estimating equations and mixed-effects models for continuous outcomes using R software.
  • To evaluate the performance of four popular R functions ('geese', 'gls', 'lme', 'lmer') in simulation and data analysis.

Main Methods:

  • A simulation study was conducted to compare the mean squared error of parameter estimation and the coverage proportion of 95% confidence intervals.
  • A data example was used to compare the estimation of intervention effects and intra-class correlation.
  • The R functions 'geese', 'gls', 'lme', and 'lmer' were utilized for implementing the statistical models.

Main Results:

  • The 'lme' function exhibited the shortest computation time.
  • No significant differences in mean squared error were observed among the four functions.
  • 'lmer' provided superior fixed-effects coverage with a small number of clusters (10).
  • 'gls' produced confidence intervals for intra-class correlation close to nominal scale, while 'geese' yielded a negative estimate in the data analysis.

Conclusions:

  • The 'gls' function is efficient for estimating intra-class correlation and its confidence interval.
  • Confidence intervals for intra-class correlation may not be consistently obtainable when within-group correlation is high (e.g., 0.5).