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Framework for Drug Formulary Decision Using Multiple-Criteria Decision Analysis.

Vusal Babashov1, Sarah Ben Amor1, Gilles Reinhardt1

  • 1Telfer School of Management, University of Ottawa, Ottawa, ON, Canada.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|April 28, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using multiple-criteria decision analysis (MCDA) to predict drug coverage decisions. The approach infers preferences from past reviews, simplifying complex assessments for review boards.

Keywords:
decision supportformulary designmultiple-criteria decision analysis

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Area of Science:

  • Health economics
  • Decision science
  • Pharmaceutical policy

Background:

  • Drug review for coverage is complex, involving conflicting evidence from multiple criteria.
  • Existing methods require significant expertise and time from review boards.
  • Multiple-criteria decision analysis (MCDA) offers tools for complex decisions with conflicting objectives.

Purpose of the Study:

  • To propose and validate a novel MCDA approach for inferring utility models from past drug reviews.
  • To predict coverage decisions for new oncology drugs using historical data.
  • To streamline the drug review process by inferring preferences rather than direct assessment.

Main Methods:

  • Utilized UTADIS(GMS), an extension of the UTilitiés Additives DIScriminantes approach, for preference disaggregation.
  • Applied the method to a portfolio of oncology drugs reviewed in Canada (2011-2017).
  • Derived global and marginal utility functions consistent with review board recommendations.

Main Results:

  • The method generated utility values for each drug submission.
  • Identified thresholds for partitioning utility values based on submission outcomes.
  • Scenario analyses demonstrated the predictive accuracy of the MCDA model.

Conclusions:

  • Preference disaggregation provides an indirect, data-driven method to elicit utility functions.
  • This approach reduces cognitive burden on decision-making bodies.
  • The model can validate past decisions and predict future drug coverage recommendations.