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Related Experiment Videos

Cluster randomized trials: another problem for cost-effectiveness ratios.

Terry N Flynn1, Tim J Peters

  • 1Department of Social Medicine, University of Bristol, Bristol, UK. terry.flynn@bristol.ac.uk

International Journal of Technology Assessment in Health Care
|August 23, 2005
PubMed
Summary
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Cluster-adjusted bootstrap methods for cost-effectiveness analysis in cluster randomized trials (CRTs) may be acceptable with over 24 clusters per arm. Further research is needed for adequate economic data analysis.

Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Trials

Background:

  • Cost-effectiveness analysis (CEA) in cluster randomized trials (CRTs) presents unique statistical challenges.
  • Nonparametric bootstrap methods offer advantages for skewed data and correlated costs/effects.
  • Existing cluster bootstrap procedures have theoretical limitations requiring practical investigation.

Purpose of the Study:

  • To evaluate the suitability of cluster-adjusted nonparametric bootstrap for CEA in CRTs.
  • To assess the performance of bootstrap methods under various conditions of cost and effect correlations.
  • To identify thresholds for acceptable performance of bootstrap procedures in CRT economic evaluations.

Main Methods:

  • Monte Carlo simulations were employed to compare two bootstrap methods.

Related Experiment Videos

  • Confidence interval coverage for incremental cost-effectiveness ratios (ICERs) was estimated.
  • The impact of the number of clusters and data correlation on bootstrap performance was examined.

    • Main Results:

    • Bootstrap methods tended to produce overly narrow confidence intervals for ICERs.
    • Acceptable results were observed when the number of clusters per arm exceeded 24.
    • A bootstrap method resampling individuals and clusters performed poorly with correlated cost and effectiveness data.

    Conclusions:

    • Further investigation into more sophisticated bootstrap procedures is necessary for adequate analysis of economic data from CRTs.
    • Additional research on alternative methods, such as the net benefit approach, is recommended.
    • Careful consideration of bootstrap method selection is crucial for reliable economic evaluations in CRTs.