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Which interactions matter in economic evaluations? A systematic review and simulation study.

Helen Dakin1, Alastair Gray2

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BMC Medical Research Methodology
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Summary

Interactions in economic evaluations of factorial randomized controlled trials (RCTs) are common but rarely change conclusions. Accounting for interactions, regardless of statistical significance, minimizes opportunity costs in treatment decisions.

Keywords:
Cost-effectiveness analysisEconomic evaluationFactorial randomised controlled trialInteractionsSimulationSystematic review

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Area of Science:

  • Health Economics
  • Clinical Trials Methodology
  • Statistical Analysis

Background:

  • Economic evaluations alongside factorial randomized controlled trials (RCTs) are crucial for decision-making.
  • Understanding interactions in costs and quality-adjusted life-years (QALYs) is vital for accurate net benefit assessments.
  • Factorial RCTs allow for the investigation of multiple interventions simultaneously, increasing the potential for interaction effects.

Purpose of the Study:

  • To assess the magnitude of interactions in costs, QALYs, and net benefits from published economic evaluations of factorial RCTs.
  • To evaluate the impact of different analytical methods on study results.
  • To compare criteria for identifying significant interactions for inclusion in analyses.

Main Methods:

  • Systematic review of economic evaluations alongside factorial RCTs.
  • Development of the interaction-effect ratio to quantify interaction magnitude.
  • Assessment of analysis changes (including/excluding interactions) on conclusions.
  • Simulation using generated data to evaluate performance of different criteria for interaction inclusion.

Main Results:

  • Large interactions for economic endpoints were frequent in 40 identified studies.
  • Interactions rarely altered the overall conclusions of the economic evaluations.
  • The interaction-effect ratio provided a measure of interaction magnitude relative to main effects.

Conclusions:

  • Accounting for all interactions or those above a certain size (irrespective of statistical significance) minimizes opportunity costs in factorial RCT analyses.
  • This approach helps ensure the adoption of treatments with the highest true net benefit.
  • Careful consideration of interactions is essential for robust economic evaluations of factorial RCTs.