ArticleMedical decision making : an international journal of the Society for Medical Decision Making2021
Bayesian versus Empirical Calibration of Microsimulation Models: A Comparative Analysis.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- 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
- Natural history models for lung Cancer: A scoping review.Lung cancer (Amsterdam, Netherlands) · 2025Article
- Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Article
- Cost effectiveness of non-drug interventions that reduce nursing home admissions for people living with dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Guidance on the use of complex systems models for economic evaluations of public health interventions.Health economics · 2023Review
- 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
- Using mathematical modeling to estimate time-independent cancer chemotherapy efficacy parameters.In silico pharmacology · 2022Article
- Development, Testing, and Calibration of LINCS: A New Microsimulation Model of Maternal and Fetal Cytomegalovirus Infection in the United States.MDM policy & practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Calibration of a microsimulation model (MSM) is a challenging but crucial step for the development of a valid model. Numerous calibration methods for MSMs have been suggested in the literature, most of which are usually adjusted to the specific needs of the model and based on subjective criteria for the selection of optimal parameter values. This article compares 2 general approaches for calibrating MSMs used in medical decision making, a Bayesian and an empirical approach. We use as a tool the MIcrosimulation Lung Cancer (MILC) model, a streamlined, continuous-time, dynamic MSM that describes the natural history of lung cancer and predicts individual trajectories accounting for age, sex, and smoking habits. We apply both methods to calibrate MILC to observed lung cancer incidence rates from the Surveillance, Epidemiology and End Results (SEER) database. We compare the results from the 2 methods in terms of the resulting parameter distributions, model predictions, and efficiency. Although the empirical method proves more practical, producing similar results with smaller computational effort, the Bayesian method resulted in a calibrated model that produced more accurate outputs for rare events and is based on a well-defined theoretical framework for the evaluation and interpretation of the calibration outcomes. A combination of the 2 approaches is an alternative worth considering for calibrating complex predictive models, such as microsimulation models.
Indexed as
Identifiers
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.