ArticleMedical decision making : an international journal of the Society for Medical Decision Making2018
Nonidentifiability in Model Calibration and Implications for Medical Decision Making.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
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- Developing and Calibrating a Colorectal Cancer Microsimulation Model for Northern Ireland.PharmacoEconomics · 2026Article
- PRE-CISE: A PRE-calibration Coverage, Identifiability, and SEnsitivity analysis workflow to streamline model calibration.medRxiv : the preprint server for health sciences · 2026Article
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- The cost of non-drug interventions that improve function and reduce dementia-related behaviors.BMC geriatrics · 2025Article
- Article
- Incorporating Additional Evidence as Prior Information to Resolve Non-Identifiability in Bayesian Disease Model Calibration: A Tutorial.Statistics in medicine · 2025Article
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- Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Emulator-based Bayesian calibration of the CISNET colorectal cancer models.medRxiv : the preprint server for health sciences · 2024Article
- A scoping review of mathematical models covering Alzheimer's disease progression.Frontiers in neuroinformatics · 2024Article
- Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Article
- Organ Transplantation Outcomes of Deceased Organ Donors in Organ Procurement Organization-Based Recovery Facilities Versus Acute-Care Hospitals.Progress in transplantation (Aliso Viejo, Calif.) · 2023Article
- Upper age limits for US male human papillomavirus vaccination for oropharyngeal cancer prevention: a microsimulation-based modeling study.Journal of the National Cancer Institute · 2023Article
- Optimal timing and effectiveness of COVID-19 outbreak responses in China: a modelling study.BMC public health · 2022Article
- Calibrating Natural History of Cancer Models in the Presence of Data Incompatibility: Problems and Solutions.PharmacoEconomics · 2022Article
- CDX2 Biomarker Testing and Adjuvant Therapy for Stage II Colon Cancer: An Exploratory Cost-Effectiveness Analysis.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2022Article
- Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models.Frontiers in physiology · 2022Article
- BayCANN: Streamlining Bayesian Calibration With Artificial Neural Network Metamodeling.Frontiers in physiology · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundCalibration is the process of estimating parameters of a mathematical model by matching model outputs to calibration targets. In the presence of nonidentifiability, multiple parameter sets solve the calibration problem, which may have important implications for decision making. We evaluate the implications of nonidentifiability on the optimal strategy and provide methods to check for nonidentifiability.
methodsWe illustrate nonidentifiability by calibrating a 3-state Markov model of cancer relative survival (RS). We performed 2 different calibration exercises: 1) only including RS as a calibration target and 2) adding the ratio between the 2 nondeath states over time as an additional target. We used the Nelder-Mead (NM) algorithm to identify parameter sets that best matched the calibration targets. We used collinearity and likelihood profile analyses to check for nonidentifiability. We then estimated the benefit of a hypothetical treatment in terms of life expectancy gains using different, but equally good-fitting, parameter sets. We also applied collinearity analysis to a realistic model of the natural history of colorectal cancer.
resultsWhen only RS is used as the calibration target, 2 different parameter sets yield similar maximum likelihood values. The high collinearity index and the bimodal likelihood profile on both parameters demonstrated the presence of nonidentifiability. These different, equally good-fitting parameter sets produce different estimates of the treatment effectiveness (0.67 v. 0.31 years), which could influence the optimal decision. By incorporating the additional target, the model becomes identifiable with a collinearity index of 3.5 and a unimodal likelihood profile.
conclusionsIn the presence of nonidentifiability, equally likely parameter estimates might yield different conclusions. Checking for the existence of nonidentifiability and its implications should be incorporated into standard model calibration procedures.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.