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Related Experiment Video

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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Published on: September 19, 2012

Risk aversion and uncertainty in cost-effectiveness analysis: the expected-utility, moment-generating function

Elamin H Elbasha¹

¹US Centers for Disease Control and Prevention, USA. elamin_elbasha@merck.com

Health Economics

|September 24, 2004

Summary

This study introduces an exponential utility-moment-generating function approach for cost-effectiveness analysis (CEA) to model healthcare interventions with uncertain costs and effects, extending beyond traditional methods.

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Area of Science:

Health Economics
Decision Analysis
Risk Management

Background:

Patient-level data from clinical trials enables advanced cost-effectiveness analysis (CEA).
Existing CEA methods often focus on confidence intervals for the incremental cost-effectiveness ratio (ICER).
A subset of research highlights the importance of risk preferences and mean-variance analysis in investment decisions.

Purpose of the Study:

To present an exponential utility-moment-generating function approach for CEA.
To model choices for healthcare interventions with uncertain costs and effects.
To extend the literature on risk preferences in health economic evaluations.

Main Methods:

Utilizes an exponential utility function, implying constant absolute risk aversion.
Leverages the moment-generating function for convenient expressions of expected utility.
Applies optimization techniques to characterize resource allocation and derive a summary measure.

Main Results:

The exponential utility-moment-generating function approach is a natural extension for modeling uncertain costs and effects.
The mean-variance approach is identified as a special case of this broader framework.
A new summary measure is derived for situations where risk neutrality does not hold, compared to the standard ICER.

Conclusions:

The proposed framework offers a more comprehensive approach to CEA by incorporating risk preferences.
Accurate estimation of cost and effect distributions and their parameters is crucial for reliable results.
The method provides a valuable tool for resource allocation decisions in healthcare under uncertainty.