Decision Modelling For Health Economic Evaluation
Abstract
In financially constrained health systems across the world, increasing emphasis is being placed on the ability to demonstrate that health care interventions are not only effective, but also cost-effective. This book deals with decision modelling techniques that can be used to estimate the value for money of various interventions including medical devices, surgical procedures, diagnostic technologies, and pharmaceuticals. Particular emphasis is placed on the importance of the appropriate representation of uncertainty in the evaluative process and the implication this uncertainty has for decision making and the need for future research. This highly practical guide takes the reader through the key principles and approaches of modelling techniques. It begins with the basics of constructing different forms of the model, the population of the model with input parameter estimates, analysis of the results, and progression to the holistic view of models as a valuable tool for informing future research exercises. Case studies and exercises are supported with online templates and solutions. This book will help analysts understand the contribution of decision-analytic modelling to the evaluation of health care programmes. ABOUT THE SERIES: Economic evaluation of health interventions is a growing specialist field, and this series of practical handbooks will tackle, in-depth, topics superficially addressed in more general health economics books. Each volume will include illustrative material, case histories and worked examples to encourage the reader to apply the methods discussed, with supporting material provided online. This series is aimed at health economists in academia, the pharmaceutical industry and the health sector, those on advanced health economics courses, and health researchers in associated fields.
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Probability distributions for parameters varied in the probabilistic sensitivity analysis were selected in accordance with best practices.
“Probability distributions for parameters that were varied in the PSA were selected in accordance with best practices [71, 72]”
Logistic distributions are natural matches for odds ratios.
“logistic for odds ratios [14]”
From Model Parameter Estimation and Uncertainty: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6 (2012) · cites this paper as [14]
Log normal distributions are natural matches for relative risks or hazard ratios.
“log normal for relative risks or hazard ratios”
From Model Parameter Estimation and Uncertainty: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6 (2012) · cites this paper as [14]
These uncertainties can be used to specify the interval for DSA or as the basis for PSA.
“These can be used to specify the interval for DSA or as the basis for PSA [14].”
From Model Parameter Estimation and Uncertainty: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6 (2012) · cites this paper as [14]
Gamma or log normal distributions are natural matches for right skew parameters.
“gamma or log normal for right skew parameters”
From Model Parameter Estimation and Uncertainty: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6 (2012) · cites this paper as [14]
Beta distributions are a natural match for binomial data.
“beta distributions are a natural match for binomial data”
From Model Parameter Estimation and Uncertainty: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6 (2012) · cites this paper as [14]
US guidelines recommend offering endocrine therapy to women whose annual risk of breast cancer exceeds 0.3%.
“offering endocrine therapy to women whose annual risk of breast cancer exceeds 0.3%.42,43”
From Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US (2021) · cites this paper as [43]
These PPV estimates are in line with risk thresholds used for primary prevention of other similarly impactful conditions.
“Notably, these PPV estimates are in line with risk thresholds used for primary prevention of other similarly impactful conditions.”
From Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US (2021) · cites this paper as [43]
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BELGIAN GUIDELINES FOR ECONOMIC EVALUATIONS: SECOND EDITION
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- Authors
- Briggs, A., Claxton, K., & Sculpher, M.
- Publisher
- Oxford University PressOxford
- Published
- 2006-08-17
- OpenAlex
- W85469694
- Cited by
- 3,153
- References
- 0
- Citations by year
- 2026: 144 · 2025: 196 · 2024: 164 · 2023: 153 · 2022: 182 · 2021: 240 · 2020: 252 · 2019: 203 · 2018: 178 · 2017: 179 · 2016: 181 · 2015: 186 · 2014: 170 · 2013: 197 · 2012: 147
- FWCI
- 67.34
- Type
- book
- Language
- en
- Open access
- closed
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Social Sciences › Economics, Econometrics and Finance › Finance › Capital Investment and Risk Analysis
- Keywords
- decision models, health economic evaluation, cost-effectiveness, uncertainty representation, decision-analytic model, value for money, health interventions, medical devices, pharmaceuticals, diagnostic technologies, health care programmes
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