ArticleBMC medical research methodology2017
Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models.
Article in BMC medical research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Calibrating Natural History of Cancer Models in the Presence of Data Incompatibility: Problems and Solutions.PharmacoEconomics · 2022Article
- Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Economic evaluation of using polygenic risk score to guide risk screening and interventions for the prevention of type 2 diabetes in individuals with high overall baseline risk.Frontiers in genetics · 2022Article
- Circulating Tumour DNA as a Potential Cost-Effective Biomarker to Reduce Adjuvant Chemotherapy Overtreatment in Stage II Colorectal Cancer.PharmacoEconomics · 2021Article
- Evaluating the resource implications of different service delivery models for offering additional genomic findings.Genetics in medicine : official journal of the American College of Medical Genetics · 2021Article
- Simulating Progression-Free and Overall Survival for First-Line Doublet Chemotherapy With or Without Bevacizumab in Metastatic Colorectal Cancer Patients Based on Real-World Registry Data.PharmacoEconomics · 2020Article
- Addressing Challenges of Economic Evaluation in Precision Medicine Using Dynamic Simulation Modeling.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2020Article
- Comparing Strategies for Modeling Competing Risks in Discrete-Event Simulations: A Simulation Study and Illustration in Colorectal Cancer.Medical decision making : an international journal of the Society for Medical Decision Making · 2019Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundParametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes.
methodsTwo approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study.
resultsAccounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes.
conclusionsModelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.
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