ArticleJournal of the American Statistical Association2009
Bayesian Calibration of Microsimulation Models.
Article in Journal of the American Statistical Association, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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
33 citing papers in PubMed.
- Validation of Colorectal Cancer Models on Long-term Outcomes from a Randomized Controlled Trial.Medical decision making : an international journal of the Society for Medical Decision Making · 2020Trial
- An updated natural history model of cervical cancer: derivation of model parameters.American journal of epidemiology · 2014Trial
- Discrete-Event Simulation Modeling Framework for Cancer Interventions and Population Health in R (DESCIPHR): An Open-Source Pipeline.PharmacoEconomics · 2026Article
- Developing and Calibrating a Colorectal Cancer Microsimulation Model for Northern Ireland.PharmacoEconomics · 2026Article
- Comparative benefits, burdens, and harms of emerging blood-based tests for colorectal cancer screening.Journal of the National Cancer Institute · 2026Article
- 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
- 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
- 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
- Microsimulation Model Calibration with Approximate Bayesian Computation in R: A Tutorial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Sequentially calibrating a Bayesian microsimulation model to incorporate new information and assumptions.BMC medical informatics and decision making · 2022Article
- Using mathematical modeling to estimate time-independent cancer chemotherapy efficacy parameters.In silico pharmacology · 2022Article
- Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models.Frontiers in physiology · 2022Article
- Bayesian versus Empirical Calibration of Microsimulation Models: A Comparative Analysis.Medical decision making : an international journal of the Society for Medical Decision Making · 2021Article
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- BayCANN: Streamlining Bayesian Calibration With Artificial Neural Network Metamodeling.Frontiers in physiology · 2021Article
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- MICROSIMULATION MODEL CALIBRATION USING INCREMENTAL MIXTURE APPROXIMATE BAYESIAN COMPUTATION.The annals of applied statistics · 2019Article
- Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators.Medical decision making : an international journal of the Society for Medical Decision Making · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are characterized by simulation of individual event histories for an idealized population of interest. Microsimulation models are complex and invariably include parameters that are not well informed by existing data. Therefore, a key component of model development is the choice of parameter values. Microsimulation model parameter values are selected to reproduce expected or known results though the process of model calibration. Calibration may be done by perturbing model parameters one at a time or by using a search algorithm. As an alternative, we propose a Bayesian method to calibrate microsimulation models that uses Markov chain Monte Carlo. We show that this approach converges to the target distribution and use a simulation study to demonstrate its finite-sample performance. Although computationally intensive, this approach has several advantages over previously proposed methods, including the use of statistical criteria to select parameter values, simultaneous calibration of multiple parameters to multiple data sources, incorporation of information via prior distributions, description of parameter identifiability, and the ability to obtain interval estimates of model parameters. We develop a microsimulation model for colorectal cancer and use our proposed method to calibrate model parameters. The microsimulation model provides a good fit to the calibration data. We find evidence that some parameters are identified primarily through prior distributions. Our results underscore the need to incorporate multiple sources of variability (i.e., due to calibration data, unknown parameters, and estimated parameters and predicted values) when calibrating and applying microsimulation models.
Identifiers
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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.