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Related Experiment Videos

Methods for analyzing cost effectiveness data from cluster randomized trials.

Max O Bachmann1, Lara Fairall, Allan Clark

  • 1School of Medicine, Health Policy and Practice, University of East Anglia, Norwich, UK. m.bachmann@uea.ac.uk

Cost Effectiveness and Resource Allocation : C/E
|September 8, 2007
PubMed
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Cost-effectiveness analysis alongside cluster randomized trials requires accounting for study design. Appropriate methods, like joint modeling and Bayesian approaches, reveal greater uncertainty than previously recognized.

Area of Science:

  • Health economics
  • Biostatistics
  • Clinical trial design

Background:

  • Cost-effectiveness analysis (CEA) quantifies uncertainty in randomized trials.
  • Cluster randomization increases uncertainty compared to individual randomization.
  • Incorrect analysis of cluster randomized trial data is common.

Purpose of the Study:

  • To demonstrate appropriate analytic methods for CEA in cluster randomized trials.
  • To highlight the impact of cluster randomization on uncertainty estimation.
  • To provide guidance on analyzing cost and outcome data from cluster trials.

Main Methods:

  • Joint modeling of costs and effects using bootstrap sampling.
  • Joint modeling of costs and effects with Bayesian hierarchical models.

Related Experiment Videos

  • Linear regression of net benefits with robust error adjustment and hierarchical models.

    • Main Results:

    • All five demonstrated methods yielded similar results.
    • Accounting for cluster randomization increased the quantified uncertainty.
    • Results underscore the importance of design-aware analysis.

    Conclusions:

    • CEA in cluster randomized trials must account for the specific study design.
    • Multiple statistically sound methods are available in standard software.
    • Accurate uncertainty quantification is crucial for decision-making.