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Cost-Effectiveness in Health and Medicine$
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Peter J. Neumann, Theodore G. Ganiats, Louise B. Russell, Gillian D. Sanders, and Joanna E. Siegel

Print publication date: 2016

Print ISBN-13: 9780190492939

Published to Oxford Scholarship Online: November 2016

DOI: 10.1093/acprof:oso/9780190492939.001.0001

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Reflecting Uncertainty in Cost-Effectiveness Analysis

Reflecting Uncertainty in Cost-Effectiveness Analysis

Chapter:
(p.289) 11 Reflecting Uncertainty in Cost-Effectiveness Analysis
Source:
Cost-Effectiveness in Health and Medicine
Author(s):

Mark J. Sculpher

Anirban Basu

Karen M. Kuntz

David O. Meltzer

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780190492939.003.0011

The key objective of uncertainty analysis is to support better decision making. Uncertainty analysis can help inform the standard decision options of “accept” or “reject,” but extend these options to include, for example, adoption alongside research or adoption only in the context of research. Since the original Panel’s report, a range of analytical methods has emerged to guide these decisions. We note that deterministic sensitivity analysis can provide useful insights into model behavior and validation, but emphasize that probabilistic sensitivity analysis provides stronger analytical support for decision making. The Second Panel therefore urges that structural uncertainties be tested in analyses, that decision uncertainty be presented using probabilities for specified cost-effectiveness thresholds or cost-effectiveness acceptability curves (CEACs), and that expected value-of-information analysis be used to guide decision making fully by quantifying the value of generating additional evidence.

Keywords:   Uncertainty analysis, sensitivity analysis, decision uncertainty, value-of-information analysis, irreversible costs, structural uncertainty, parameter uncertainty

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