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Related Experiment Video

Updated: Feb 4, 2026

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Treatment recommendations based on network meta-analysis: Rules for risk-averse decision-makers.

A E Ades1, Annabel L Davies1, David M Phillippo1

  • 1Population Health Sciences, Bristol University Medical School, Bristol, UK.

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|February 2, 2026
PubMed
Summary

This study introduces loss-adjusted expected value (LaEV) for recommending multiple treatments in network meta-analysis (NMA). LaEV offers a valid, uncertainty-penalizing ranking system suitable for risk-averse decision-makers.

Keywords:
GRADEdecision-makingexpected valueloss-adjustmentnetwork meta-analysistreatment ranking

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Area of Science:

  • Health Economics
  • Decision Analysis
  • Biostatistics

Background:

  • Network meta-analysis (NMA) typically recommends a single treatment based on highest expected value (EV).
  • Existing methods may not adequately address decision-maker risk aversion or uncertainty.
  • Need for ranking systems that penalize uncertainty and identify clinically relevant treatment options.

Purpose of the Study:

  • To develop and evaluate novel approaches for multiple treatment recommendations in NMA.
  • To introduce and assess loss-adjusted expected value (LaEV) as a risk-averse decision criterion.
  • To compare LaEV with existing methods like GRADE and probability-based rankings.

Main Methods:

  • Introduction of loss-adjusted expected value (LaEV) and definition of desirable ranking properties.
  • Development of a two-stage recommendation process incorporating minimal clinically important difference (MCID).
  • Comparative analysis using stylized examples and 10 National Institute of Health and Care Excellence (NICE) NMAs.

Main Results:

  • LaEV reliably produces valid rankings under uncertainty and possesses desirable properties.
  • LaEV-based recommendations were more conservative than standard EV, recommending fewer treatments (median 2 fewer).
  • GRADE rules showed anomalies, sometimes failing to recommend the highest EV/LaEV treatment and privileging uncertain options.

Conclusions:

  • LaEV provides a theoretically sound, conservative, and implementable method for risk-averse decision-making in NMA.
  • A two-stage approach using MCID ensures clinically appropriate numbers of recommended treatments.
  • LaEV offers a superior alternative to standard EV, GRADE, and probability-based rankings for complex treatment comparisons.