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Related Experiment Videos

Cost utility analysis: what should be measured?

J Richardson1

  • 1National Centre for Health Program Evaluation, Monash University, Fairfield Hospital, Victoria, Australia.

Social Science & Medicine (1982)
|July 1, 1994
PubMed
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This study evaluates measurement units for cost utility analysis. Time trade-off and person trade-off techniques are superior to rating scales, magnitude estimation, and standard gamble for measuring health outcomes.

Area of Science:

  • Health Economics
  • Outcome Measurement
  • Decision Analysis

Background:

  • Cost utility analysis (CUA) is crucial for healthcare decision-making.
  • Selecting appropriate units to measure health outcomes is a persistent challenge in CUA.
  • Existing methods for quantifying utility have limitations in representing true value.

Purpose of the Study:

  • To re-examine the appropriate unit for measuring output in cost utility analysis.
  • To establish criteria for evaluating measurement units in health economics.
  • To compare the suitability of different utility measurement techniques.

Main Methods:

  • Proposed four criteria for evaluating measurement units, derived from social objectives and measurement requirements.
  • Compared utility units generated by time trade-off, person trade-off (equivalence), rating scale, magnitude estimation, and standard gamble techniques.

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  • Evaluated techniques based on the established criteria.

    • Main Results:

    • Utility, as commonly conceived, is not an appropriate basis for measurement in CUA.
    • Time trade-off and person trade-off techniques yield more satisfactory measurement units.
    • Rating scale, magnitude estimation, and standard gamble techniques are less suitable for measuring health outcomes.

    Conclusions:

    • The time trade-off and person trade-off techniques are preferred for measuring output in cost utility analysis.
    • Established criteria provide a framework for selecting robust utility measurement methods.
    • Further research should focus on refining and validating these preferred measurement techniques.