ArticleMedical decision making : an international journal of the Society for Medical Decision Making
Bayesian calibration of a natural history model with application to a population model for colorectal cancer.
Article in Medical decision making : an international journal of the Society for Medical Decision Making. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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27 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.
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- Multiobjective Calibration of Disease Simulation Models Using Gaussian Processes.Medical decision making : an international journal of the Society for Medical Decision Making · 2019Article
- Development of an Empirically Calibrated Model of Esophageal Squamous Cell Carcinoma in High-Risk Regions.BioMed research international · 2019Article
- Bayesian Methods for Calibrating Health Policy Models: A Tutorial.PharmacoEconomics · 2017Article
- Improving early diagnosis of symptomatic cancer.Nature reviews. Clinical oncology · 2016Review
- Cost Effectiveness of Colorectal Cancer Screening Interventions with Their Effects on Health Disparity Being Considered.Cancer research and treatment · 2016Article
- Cancer care coordinators in stage III colon cancer: a cost-utility analysis.BMC health services research · 2015Article
- Modelling the cost-effectiveness of public awareness campaigns for the early detection of non-small-cell lung cancer.British journal of cancer · 2015Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer natural history models are essential when evaluating screening/preventative interventions or changes to diagnostic pathways. Natural history models commonly use a state transition structure, but it is often not possible to observe the state transition probabilities required for parameterization.
aim. The work aimed to accurately represent the uncertainty in the parameters of a state transition model for the natural history of colorectal cancer by embedding the problem in the framework of Bayesian inference.
methodsThe Metropolis-Hastings algorithm was used to estimate natural history parameters and screening test characteristics by generating multiple sets of parameters from the posterior distribution, which is the probability distribution that is compatible with the observed data. Observed data included colorectal cancer incidence categorized by age and stage, autopsy data on polyp prevalence, and cancer and polyp detection rates from the first round of screening with the fecal occult blood test in England. The approach was implemented using Visual Basic.
resultsThe results were subsequently examined for convergence using the package CODA in R 2.8.0. Outputs from fitting were samples from the joint posterior distribution of the natural history parameters given the epidemiological data. The parameter sets obtained are shown to have a good fit to all the observed data sets. These parameter sets are used when running probabilistic sensitivity analysis.
conclusionThe advantages of this strategy are that it draws efficiently from a high-dimensional correlated parameter space. The algorithm is simple to code and runs overnight on a standard desktop PC. Using this method, the parameter sets are drawn according to their posterior probability given calibration data, and thus they correctly summarize the residual uncertainty in the parameter space.
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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.