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Partial factorial trials: comparing methods for statistical analysis and economic evaluation.

Helen A Dakin1, Alastair M Gray2, Graeme S MacLennan3

  • 1Health Economics Research Centre, Nuffield Department of Population Health, Old Road Campus, Headington, Oxford, OX3 7LF, UK. helen.dakin@ndph.ox.ac.uk.

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|August 18, 2018
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Summary
This summary is machine-generated.

This study compared economic evaluation methods for partial factorial trials. The Bayesian bootstrap method effectively handles interactions, offering a robust alternative to standard analyses for clinical and economic endpoints.

Keywords:
Bayesian bootstrapCost-utility analysisFactorial designPartial factorial trialRandomised controlled trial

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Area of Science:

  • Health Economics
  • Clinical Trial Methodology
  • Biostatistics

Background:

  • Partial factorial trials involve complex patient randomization across multiple treatment comparisons.
  • Evaluating economic outcomes in these trials requires careful consideration of simultaneous treatment effects and interactions.

Purpose of the Study:

  • To compare analytical methods for economic evaluations in partial factorial trials.
  • To assess the impact of simultaneous factor consideration versus independent conclusions on trial analyses.

Main Methods:

  • Estimated costs and quality-adjusted life years (QALYs) in 2252 knee arthroplasty patients.
  • Compared "at-the-margins," "inside-the-table," and Bayesian bootstrap analyses.
  • Investigated incremental costs, QALYs, and net benefits.

Main Results:

  • Qualitative interactions were found for costs, QALYs, and net benefits.
  • Bayesian bootstrapping yielded smaller standard errors and consistent conclusions, accounting for interactions.
  • The "inside-the-table" analysis produced different conclusions regarding net benefits.

Conclusions:

  • Analyses of partial factorial trials must explore and assess sensitivity to interactions.
  • Bayesian bootstrap is a viable alternative for analyzing clinical or economic endpoints, accommodating interactions.
  • Standard "at-the-margins" analysis may be misleading with significant interactions or non-representative treatment proportions.