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Targeting Assessments of Magnetic Resonance Imaging in suspected Multiple sclerosis

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Listed:
  • Cathleen Mooney
  • Alvin I. Mushlin
  • Charles E. Phelps

Abstract

Decision-analytic methods can be valuable for targeting research in technology assessment. They can indicate whether further evaluation of a technology is warranted, and if so, which variables are key determinants of its clinical utility and cost-effectiveness. This approach was tested on a salient issue—whether magnetic resonance imaging (MR) should be used in evaluating patients with mild neurologic symptoms who might have multiple sclerosis (MS). The authors developed a decision-analytic model to assess the expected utility and costs associated with immediately using MR in this situation, compared with waiting for further symptoms to emerge before testing. Sensitivity analyses demonstrated that priorities for technology assessment research include estimating the value of information to patients in resolving uncertainty, evaluating the impact on patients of being labeled with a diagnosis of MS, and measuring the test characteristics of MR. Key words: cost-benefit analysis; multiple sclerosis; magnetic resonance imaging; decision analysis; Markov models. (Med Decis Making 1990;10:77-94)

Suggested Citation

  • Cathleen Mooney & Alvin I. Mushlin & Charles E. Phelps, 1990. "Targeting Assessments of Magnetic Resonance Imaging in suspected Multiple sclerosis," Medical Decision Making, , vol. 10(2), pages 77-94, June.
    • Handle: RePEc:sae:medema:v:10:y:1990:i:2:p:77-94
    DOI: 10.1177/0272989X9001000201
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    References listed on IDEAS

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    1. George W. Torrance & Michael H. Boyle & Sargent P. Horwood, 1982. "Application of Multi-Attribute Utility Theory to Measure Social Preferences for Health States," Operations Research, INFORMS, vol. 30(6), pages 1043-1069, December.
    2. David G. Simon & Michael F. Lubin, 1985. "Cost-Effectiveness of Computerized Tomography and Magnetic Resonance Imaging in Dementia," Medical Decision Making, , vol. 5(3), pages 335-351, August.
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    Cited by:

    1. Gordon B. Hazen & James M. Pellissier & Jayavel Sounderpandian, 1998. "Stochastic-Tree Models in Medical Decision Making," Interfaces, INFORMS, vol. 28(4), pages 64-80, August.

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