Probabilistic analysis of cost-effectiveness models: statistical representation of parameter uncertainty

Journal article

There was a time when a simple dichotomy characterized many health economic evaluations. On the one hand there were those economic appraisals that were conducted alongside clinical trials and which commonly employed statistical methods in so-called stochastic evaluations. On the other, there was the use of decision analytic modeling to synthesize data from secondary sources in order to estimate cost-effectiveness in a deterministic fashion. Now, however, the distinctions are becoming ever more blurred. The limitations of single trials as the sole vehicle for economic appraisal is widely reported [1] and, in particular, the continued need for modeling to adapt trial-based analyses is well understood [2]. Furthermore, the use of probabilistic sensitivity analysis to represent uncertainty in modeling studies offers the opportunity to make statistical statements about the impact of parameter uncertainty for cost-effectiveness estimates from deterministic models.

Authors: Briggs, A

• Publication status: Published
• Peer review status: Peer reviewed
• Journal: Value in Health
• Volume: 8
• Issue: 1
• Pages: 1-2
• Publication date: 2005-01-01
• Edition: Publisher's version
• DOI: 10.1111/j.1524-4733.2005.08101.x
• ISSN: 1098-3015
• Language: English
• Subjects: Probability theory and stochastic processes