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Experiences of Structured Elicitation for Model-Based Cost-Effectiveness Analyses.

Marta O Soares1, Linda Sharples2, Alec Morton3

  • 1Centre for Health Economics, University of York, York, UK.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|June 19, 2018
PubMed
Summary
This summary is machine-generated.

Structured expert elicitation (SEE) helps when cost-effectiveness data is missing, but methodological choices require more research. This review highlights challenges and future research needs for better healthcare technology assessments.

Keywords:
Bayesiancost effectivenessdecision modelingelicitationexpert judgmentsubjective

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Area of Science:

  • Health economics
  • Decision science
  • Evidence synthesis

Background:

  • Empirical evidence for healthcare technology cost-effectiveness is often limited or absent.
  • Expert judgment is frequently required to inform cost-effectiveness estimates.
  • Structured expert elicitation (SEE) offers formal methods to quantify expert beliefs, but methodological research is scarce.

Purpose of the Study:

  • To review applications of SEE in cost-effectiveness modeling.
  • To summarize the basis for methodological choices in SEE applications.
  • To document challenges encountered in the design, conduct, and analysis of SEE.

Main Methods:

  • Systematic review of published applications of SEE in cost-effectiveness modeling.
  • Extraction of methods, criteria for choices, and reported issues/challenges.
  • Categorization and grouping of identified issues and challenges.

Main Results:

  • Considerable heterogeneity exists in the methods employed for SEE.
  • Authors report significant methodological uncertainty in justifying their choices.
  • Key challenges include between-expert variation, lack of quantitative training in experts, diverse parameter types, validity assessment needs, and integration with behavioral research.

Conclusions:

  • Experiences with SEE highlight specific constraints and needs.
  • Further methodological research is needed, particularly concerning expert variation, training, parameter types, validity, and debiasing strategies.
  • Findings can inform the development of guidance and future research in SEE for cost-effectiveness analysis.