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Using prediction intervals from random-effects meta-analyses in an economic model.

Conor Teljeur1, Michelle O'Neill1, Patrick Moran1

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Prediction intervals in meta-analysis provide a wider range for treatment effects than confidence bounds, better reflecting technology impact. This study highlights their importance in cost-utility analyses for robot-assisted prostatectomy.

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Area of Science:

  • Health Economics
  • Medical Technology Assessment
  • Statistical Methodology

Background:

  • Incorporating treatment effect estimates from random-effects meta-analysis into cost-utility analyses requires careful consideration of uncertainty.
  • Confidence bounds narrowly define average treatment effects, while prediction intervals capture a wider range of potential outcomes.
  • Robot-assisted radical prostatectomy serves as a case study to evaluate these different approaches.

Purpose of the Study:

  • To investigate the impact of using confidence bounds versus prediction intervals from random-effects meta-analyses on cost-utility analysis.
  • To assess how different uncertainty measures influence the estimated cost-effectiveness of robot-assisted surgery.

Main Methods:

  • A cost-utility analysis was performed using an economic model for robot-assisted prostatectomy.
  • Clinical effectiveness data were synthesized using random-effects meta-analysis of peer-reviewed literature.
  • The analysis compared results obtained using confidence bounds versus prediction intervals for treatment effect estimates.

Main Results:

  • Confidence bounds yielded an Incremental Cost-Effectiveness Ratio (ICER) range of €13,752 to €68,861/QALY.
  • Prediction intervals resulted in a wider ICER range (-€135,244 to €239,166/QALY), indicating greater uncertainty.
  • Using prediction intervals showed a 4.2% probability of robot-assisted surgery reducing Quality-Adjusted Life Years (QALYs).

Conclusions:

  • Prediction intervals, unlike confidence bounds, do not alter the point estimate of treatment effect.
  • In meta-analyses with significant heterogeneity, prediction intervals yield broader treatment effect ranges, enhancing the reflection of technology's real-world impact.
  • The use of prediction intervals in cost-utility analyses improves the representation of uncertainty and potential outcomes.