Belief elicitation to populate health economic models of medical diagnostic devices in development

Appl Health Econ Health Policy. 2014 Jun;12(3):327-34. doi: 10.1007/s40258-014-0092-y.

Abstract

Background and objective: Bayesian methods can be used to elicit experts' beliefs about the clinical value of healthcare technologies. This study investigates a belief-elicitation method for estimating diagnostic performance in an early stage of development of photoacoustic mammography (PAM) imaging versus magnetic resonance imaging (MRI) for detecting breast cancer.

Research design: Eighteen experienced radiologists ranked tumor characteristics regarding their importance to detect malignancies. With reference to MRI, radiologists estimated the true positives and negatives of PAM using the variable interval method. An overall probability density function was determined using linear opinion pooling, weighted for individual experts' experience.

Result: The most important tumor characteristics are mass margins and mass shape. Respondents considered MRI the better technology to visualize these characteristics. Belief elicitation confirmed this by providing an overall sensitivity of PAM ranging from 58.9 to 85.1% (mode 75.6%) and specificity ranging from 52.2 to 77.6% (mode 66.5%).

Conclusion: Belief elicitation allowed estimates to be obtained for the expected diagnostic performance of PAM, although radiologists expressed difficulties in doing so. Heterogeneity within and between experts reflects this uncertainty and the infancy of PAM. Further clinical trials are required to validate the extent to which this belief-elicitation method is predictive for observed test performance.

MeSH terms

  • Attitude of Health Personnel*
  • Breast / pathology
  • Breast Neoplasms / diagnosis
  • Breast Neoplasms / pathology
  • Equipment and Supplies* / economics
  • Female
  • Humans
  • Magnetic Resonance Imaging
  • Mammography / instrumentation
  • Mammography / methods
  • Models, Economic*
  • Photoacoustic Techniques / instrumentation
  • Photoacoustic Techniques / methods
  • Radiology / statistics & numerical data