ArticleMedical decision making : an international journal of the Society for Medical Decision Making2015
Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial.
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 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Calibration of transmission-dynamic infectious disease models: A scoping review and reporting framework.PLoS computational biology · 2025Article
- BayesianFitForecast: a user-friendly R toolbox for parameter estimation and forecasting with ordinary differential equations.BMC medical informatics and decision making · 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
- Tabby2: a user-friendly web tool for forecasting state-level TB outcomes in the United States.BMC medicine · 2023Article
- Article
- Calibrating Natural History of Cancer Models in the Presence of Data Incompatibility: Problems and Solutions.PharmacoEconomics · 2022Article
- Global estimates of paediatric tuberculosis incidence in 2013-19: a mathematical modelling analysis.The Lancet. Global health · 2022Article
- Disease progression and mortality with untreated HIV infection: evidence synthesis of HIV seroconverter cohorts, antiretroviral treatment clinical cohorts and population-based survey data.Journal of the International AIDS Society · 2021Article
- Time Since Infection and Risks of Future Disease for Individuals with Mycobacterium tuberculosis Infection in the United States.Epidemiology (Cambridge, Mass.) · 2021Article
- Modelling the combined impact of interventions in averting deaths during a synthetic-opioid overdose epidemic.Addiction (Abingdon, England) · 2019Article
- Joining and splitting models with Markov melding.Bayesian analysis · 2019Article
- Prospects for Tuberculosis Elimination in the United States: Results of a Transmission Dynamic Model.American journal of epidemiology · 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
- Extrapolating Survival from Randomized Trials Using External Data: A Review of Methods.Medical decision making : an international journal of the Society for Medical Decision Making · 2017Review
- Validation of Models Used to Inform Colorectal Cancer Screening Guidelines: Accuracy and Implications.Medical decision making : an international journal of the Society for Medical Decision Making · 2016Article
- Validation Is Necessary but Insufficient.Medical decision making : an international journal of the Society for Medical Decision Making · 2015Article
- Quantifying demographic and socioeconomic transitions for computational epidemiology: an open-source modeling approach applied to India.Population health metrics · 2015Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Decision-analytic models must often be informed using data that are only indirectly related to the main model parameters. The authors outline how to implement a Bayesian synthesis of diverse sources of evidence to calibrate the parameters of a complex model. A graphical model is built to represent how observed data are generated from statistical models with unknown parameters and how those parameters are related to quantities of interest for decision making. This forms the basis of an algorithm to estimate a posterior probability distribution, which represents the updated state of evidence for all unknowns given all data and prior beliefs. This process calibrates the quantities of interest against data and, at the same time, propagates all parameter uncertainties to the results used for decision making. To illustrate these methods, the authors demonstrate how a previously developed Markov model for the progression of human papillomavirus (HPV-16) infection was rebuilt in a Bayesian framework. Transition probabilities between states of disease severity are inferred indirectly from cross-sectional observations of prevalence of HPV-16 and HPV-16-related disease by age, cervical cancer incidence, and other published information. Previously, a discrete collection of plausible scenarios was identified but with no further indication of which of these are more plausible. Instead, the authors derive a Bayesian posterior distribution, in which scenarios are implicitly weighted according to how well they are supported by the data. In particular, we emphasize the appropriate choice of prior distributions and checking and comparison of fitted models.
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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.