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Cost-effectiveness analysis with unordered decisions.

Francisco Javier Díez1, Manuel Luque1, Manuel Arias1

  • 1Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain.

Artificial Intelligence in Medicine
|June 15, 2021
PubMed
Summary

Decision Analysis Networks (DANs) enable cost-effectiveness analysis (CEA) for complex medical decisions with unordered choices. This method efficiently evaluates interventions, providing valuable insights for healthcare economic evaluations.

Keywords:
Cost-effectiveness analysisDecision analysis networksDecision treesInfluence diagramsProbabilistic graphical models

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Area of Science:

  • Decision Analysis
  • Health Economics
  • Medical Informatics

Background:

  • Cost-effectiveness analysis (CEA) is crucial for evaluating medical interventions but is limited by traditional methods like decision trees for complex scenarios.
  • Influence diagrams can handle larger problems but require decisions to be totally ordered, which is often not the case in clinical practice.

Purpose of the Study:

  • To develop a novel method for cost-effectiveness analysis (CEA) applicable to problems with unordered or partially ordered decisions.
  • To address limitations in modeling complex decision-making processes in healthcare, such as optimal diagnostic test sequencing.

Main Methods:

  • Introduction of Decision Analysis Networks (DANs), a new probabilistic graphical model similar to Bayesian networks and influence diagrams.
  • Development and experimental evaluation of an algorithm for assessing DANs based on cost and effectiveness criteria.
  • Illustration using a hypothetical case and a real-world application in non-small cell lung cancer staging.

Main Results:

  • DAN evaluation yields intervals for willingness to pay, delineated by incremental cost-effectiveness ratios (ICERs).
  • Cost, effectiveness, and optimal intervention are determined for each willingness-to-pay interval.
  • The approach demonstrates computational efficiency for problems with multiple unordered decisions.

Conclusions:

  • Decision Analysis Networks (DANs) provide a viable solution for modeling and evaluating complex cost-effectiveness problems with unordered decisions.
  • The developed method is computationally efficient and can be implemented using open-source tools like OpenMarkov.
  • This facilitates more robust economic evaluations in healthcare settings with intricate decision structures.