ArticlePharmacoEconomics2017
Bayesian Methods for Calibrating Health Policy Models: A Tutorial.
Article in PharmacoEconomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it, 80 citations in OpenAlex.
- Bayesian inference in racial health inequity analyses for noncommunicable diseases: a systematic review.Systematic reviews · 2025Pooled it
- Calibration of individual-based models to epidemiological data: A systematic review.PLoS computational biology · 2020Pooled it
- A Metamodel-Based General-Purpose Autocalibration Tool for Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Developing and Calibrating a Colorectal Cancer Microsimulation Model for Northern Ireland.PharmacoEconomics · 2026Article
- Discrete-Event Simulation Modeling Framework for Cancer Interventions and Population Health in R (DESCIPHR): An Open-Source Pipeline.PharmacoEconomics · 2026Article
- Article
- Hepatitis C virus transmission among people who inject drugs in rural United States: mathematical modeling study using stochastic agent-based network simulation.American journal of epidemiology · 2026Article
- PRE-CISE: A PRE-calibration Coverage, Identifiability, and SEnsitivity analysis workflow to streamline model calibration.medRxiv : the preprint server for health sciences · 2026Article
- Cost-Effectiveness of Acceptable-Quality Deceased Donor Kidneys for Transplant in Older Candidates.JAMA network open · 2026Article
- Population-Level Health Benefits and Harms Associated With Buprenorphine/Naloxone vs Methadone.JAMA network open · 2025Article
- Calibration of transmission-dynamic infectious disease models: A scoping review and reporting framework.PLoS computational biology · 2025Article
- Quantifying the number of people who would benefit from HIV pre-exposure prophylaxis (PrEP) in the United States: a comparison of behavioral, acquisition-risk based, and economic metrics.medRxiv : the preprint server for health sciences · 2025Article
- Incorporating Additional Evidence as Prior Information to Resolve Non-Identifiability in Bayesian Disease Model Calibration: A Tutorial.Statistics in medicine · 2025Article
- Using Bayesian evidence synthesis to quantify uncertainty in population trends in smoking behaviour.Statistical methods in medical research · 2025Article
- Using Joint Longitudinal and Time-to-Event Models to Improve the Parameterization of Chronic Disease Microsimulation Models: an Application to Cardiovascular Disease.medRxiv : the preprint server for health sciences · 2024Article
- The long-term effects of domestic and international tuberculosis service improvements on tuberculosis trends within the USA: a mathematical modelling study.The Lancet. Public health · 2024Article
- Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Assessing thresholds of resistance prevalence at which empiric treatment of gonorrhea should change among men who have sex with men in the US: A cost-effectiveness analysis.PLoS medicine · 2024Article
- Quantifying gaps in the tuberculosis care cascade in Brazil: A mathematical model study using national program data.PLoS medicine · 2024Article
- Emulator-based Bayesian calibration of the CISNET colorectal cancer models.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Mathematical simulation models are commonly used to inform health policy decisions. These health policy models represent the social and biological mechanisms that determine health and economic outcomes, combine multiple sources of evidence about how policy alternatives will impact those outcomes, and synthesize outcomes into summary measures salient for the policy decision. Calibrating these health policy models to fit empirical data can provide face validity and improve the quality of model predictions. Bayesian methods provide powerful tools for model calibration. These methods summarize information relevant to a particular policy decision into (1) prior distributions for model parameters, (2) structural assumptions of the model, and (3) a likelihood function created from the calibration data, combining these different sources of evidence via Bayes' theorem. This article provides a tutorial on Bayesian approaches for model calibration, describing the theoretical basis for Bayesian calibration approaches as well as pragmatic considerations that arise in the tasks of creating calibration targets, estimating the posterior distribution, and obtaining results to inform the policy decision. These considerations, as well as the specific steps for implementing the calibration, are described in the context of an extended worked example about the policy choice to provide (or not provide) treatment for a hypothetical infectious disease. Given the many simplifications and subjective decisions required to create prior distributions, model structure, and likelihood, calibration should be considered an exercise in creating a reasonable model that produces valid evidence for policy, rather than as a technique for identifying a unique theoretically optimal summary of the evidence.
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What OpenQuestion holds
Registered trials
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.