ArticlePharmacoEconomics2004
Incorporation of statistical uncertainty in health economic modelling studies using second-order Monte Carlo simulations.
Article in PharmacoEconomics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Devices for remote continuous monitoring of people with Parkinson's disease: a systematic review and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2024Pooled it
- On the Use of Markov Models in Pharmacoeconomics: Pros and Cons and Implications for Policy Makers.Frontiers in public health · 2020Article
- Cost-effectiveness of primary prevention of paediatric asthma: a decision-analytic model.The European journal of health economics : HEPAC : health economics in prevention and care · 2014Article
- The implications of regional and national demographic projections for future GMS costs in Ireland through to 2026.BMC health services research · 2014Article
- Modelling the cost effectiveness of treatments for Parkinson's disease: a methodological review.PharmacoEconomics · 2011Review
- Modelling methods for pharmacoeconomics and health technology assessment: an overview and guide.PharmacoEconomics · 2008Review
- Incorporation of uncertainty in health economic modelling studies.PharmacoEconomics · 2005Article
- Incorporation of uncertainty in health economic modelling studies.PharmacoEconomics · 2005Article
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1 author.
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No grant is acknowledged in the PubMed record.
Abstract
Health economic modelling studies are of interest to many parties with different responsibilities and diverging interests. Therefore, it is obvious that recognising the relevance of statistical uncertainty and dealing with it appropriately are required to obtain unbiased results from health economic modelling studies, especially when those data are being used for reimbursement decisions. In this manuscript we explore the relevance of the incorporation of statistical uncertainty in a health economic model and identify various types of statistical uncertainty. The concepts were applied to a hypothetical Markov model for a hypothetical antiparkinsonian (AP) product. The method was based on the incorporation of probability distributions in the input variables using a second-order Monte Carlo simulation and the definition of minimum relevant differences for clinical and economic input variables and outcomes. Our paper shows that the outcomes of a health economic model might be severely biased when statistical uncertainty is not taken into account, which justifies the need for the incorporation of statistical uncertainty in a health economic model.
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Registered trials
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