ArticleMedical decision making : an international journal of the Society for Medical Decision Making2015
Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling.PharmacoEconomics · 2019Article
- Combined estimation of disease progression and retention on antiretroviral therapy among treated individuals with HIV in the USA: a modelling study.The lancet. HIV · 2019Article
- Multiobjective Calibration of Disease Simulation Models Using Gaussian Processes.Medical decision making : an international journal of the Society for Medical Decision Making · 2019Article
- Nonidentifiability in Model Calibration and Implications for Medical Decision Making.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- Implementing parallel spreadsheet models for health policy decisions: The impact of unintentional errors on model projections.PloS one · 2018Article
- Bayesian Methods for Calibrating Health Policy Models: A Tutorial.PharmacoEconomics · 2017Article
- Strategies to Screen for Diabetic Retinopathy in Chinese Patients with Newly Diagnosed Type 2 Diabetes: A Cost-Effectiveness Analysis.Medicine · 2015Article
- Validation Is Necessary but Insufficient.Medical decision making : an international journal of the Society for Medical Decision Making · 2015Article
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4 authors.
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Abstract
backgroundTo identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better.
methodsWe demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting.
resultsFor the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results.
conclusionsChoices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets.
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