ArticlePloS one2019
A natural history model for planning prostate cancer testing: Calibration and validation using Swedish registry data.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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
11 citing papers in PubMed.
- Cost-Effectiveness Analysis of Prostate Cancer Screening in the UK: A Decision Model Analysis Based on the CAP Trial.PharmacoEconomics · 2022Trial
- Ten Recommendations for Modelling Cost Effectiveness of Screening: Perspectives of an International Stakeholder Group.PharmacoEconomics · 2026Review
- Development and validation of a histology-specific natural history model of ovarian cancer.American journal of obstetrics and gynecology · 2025Article
- A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Article
- Cost effectiveness analysis of prostate cancer screening strategies in Germany: A microsimulation study.International journal of cancer · 2025Article
- A Multistate Model Incorporating Relative Survival Extrapolation and Mixed Time Scales for Health Technology Assessment.PharmacoEconomics · 2025Article
- Screening for prostate cancer: evidence, ongoing trials, policies and knowledge gaps.BMJ oncology · 2023Review
- Cost-effectiveness of Prostate Cancer Screening Using Magnetic Resonance Imaging or Standard Biopsy Based on the STHLM3-MRI Study.JAMA oncology · 2022Article
- Simulation model of disease incidence driven by diagnostic activity.Statistics in medicine · 2021Article
- Article
- Personalized early detection and prevention of breast cancer: ENVISION consensus statement.Nature reviews. Clinical oncology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Recent prostate cancer screening trials have given conflicting results and it is unclear how to reduce prostate cancer mortality while minimising overdiagnosis and overtreatment. Prostate cancer testing is a partially observable process, and planning for testing requires either extrapolation from randomised controlled trials or, more flexibly, modelling of the cancer natural history. An existing US prostate cancer natural history model (Gulati et al, Biostatistics 2010;11:707-719) did not model for differences in survival between Gleason 6 and 7 cancers and predicted too few Gleason 7 cancers for contemporary Sweden. We re-implemented and re-calibrated the US model to Sweden. We extended the model to more finely describe the disease states, their time to biopsy-detectable cancer and prostate cancer survival. We first calibrated the model to the incidence rate ratio observed in the European Randomised Study of Screening for Prostate Cancer (ERSPC) together with age-specific cancer staging observed in the Stockholm PSA (prostate-specific antigen) and Biopsy Register; we then calibrated age-specific survival by disease states under contemporary testing and treatment using the Swedish National Prostate Cancer Register. After calibration, we were able to closely match observed prostate cancer incidence trends in Sweden. Assuming that patients detected at an earlier stage by screening receive a commensurate survival improvement, we find that the calibrated model replicates the observed mortality reduction in a simulation of ERSPC. Using the resulting model, we predicted incidence and mortality following the introduction of regular testing. Compared with a model of the current testing pattern, organised 8 yearly testing for men aged 55-69 years was predicted to reduce prostate cancer incidence by 14% and increase prostate cancer mortality by 2%. The model is open source and suitable for planning for effective prostate cancer screening into the future.
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Registered trials
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