Evidence map›Paper›PMID 30763406›Full record

ArticlePloS one2019

A natural history model for planning prostate cancer testing: Calibration and validation using Swedish registry data.

Andreas Karlsson, Alexandra Jauhiainen, Roman Gulati, Martin Eklund, Henrik Grönberg, Ruth Etzioni, Mark Clements

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Andreas KarlssonDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0001-7932-8372
Alexandra JauhiainenAstraZeneca, Gothenburg, Sweden.
Roman GulatiDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States of America.
Martin EklundDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Henrik GrönbergDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Ruth EtzioniDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States of America.
Mark ClementsDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Funding

Modeling to Improve Prostate Cancer Outcomes Across Diverse PopulationsU01CA199338 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI DE KONING, HARRY J, ETZIONI, RUTH D · 2015 to 2019
$5.2M
Statistical modeling to support population and translational cancer researchR50CA221836 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Roman Gulati · 2017 to 2026
$2.0M
NCI NIH HHS R50 CA221836NCI NIH HHS U01 CA199338
6 · The paper itself

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.

Indexed as

Early Detection of CancerModels, BiologicalProstatic NeoplasmsRegistriesAgedBiopsyDisease-Free SurvivalHumansIncidenceMaleMiddle AgedProstate-Specific AntigenSurvival RateSwedenProstate-Specific Antigen

Identifiers

PMID30763406
PMCPMC6375591

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Registered trials

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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.