Evidence map›Paper›PMID 21406620›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making

How does early detection by screening affect disease progression? Modeling estimated benefits in prostate cancer screening.

Elisabeth M Wever, Gerrit Draisma, Eveline A M Heijnsdijk, Harry J de Koning

Open access · greenAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 17% of its field
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

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.

  1. Pooled it
  2. Trial
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  6. Article
  7. Article
  8. Multi-cancer early detection tests: Attributes for clinical implementation.Cancer biomarkers : section A of Disease markers · 2025
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  10. Dying To Find Out: The Cost of Time at the Dawn of the Multicancer Early Detection Era.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023
    Review
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  12. Stage Shift as an Endpoint in Cancer Screening Trials: Implications for Evaluating Multicancer Early Detection Tests.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022
    Article
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  14. Racial/Ethnic Differences in Cancer Diagnosed after Metastasis: Absolute Burden and Deaths Potentially Avoidable through Earlier Detection.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022
    Article
  15. Review
  16. Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators.Medical decision making : an international journal of the Society for Medical Decision Making · 2019
    Article
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  18. Article
  19. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Elisabeth M WeverDepartment of Public Health, Erasmus MC Rotterdam, The Netherlands.
Gerrit DraismaDepartment of Public Health, Erasmus MC Rotterdam, The Netherlands.
Eveline A M HeijnsdijkDepartment of Public Health, Erasmus MC Rotterdam, The Netherlands.
Harry J de KoningDepartment of Public Health, Erasmus MC Rotterdam, The Netherlands.
Erasmus University Rotterdam · NLErasmus MC · NL

Funding

PSA SCREENING AND US PROSTATE CANCER TRENDSU01CA088160 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI ETZIONI, RUTH D · 2000 to 2009
$2.7M
NCI NIH HHS U01 CA088160NCI NIH HHS U01-CA88160
6 · The paper itself

Abstract

backgroundSimulation models are essential tools for estimating benefits of cancer screening programs. Such models include a screening-effect model that represents how early detection by screening followed by treatment affects disease-specific survival. Two commonly used screening-effect models are the stage-shift model, where mortality benefits are explained by the shift to more favorable stages, and the cure model, where early detection enhances the chances of cure from disease.

objectiveThis article describes commonly used screening-effect models and analyses their predicted mortality benefit in a model for prostate cancer screening.

methodThe MISCAN simulation model was used to predict the reduction of prostate cancer mortality in the European Randomized Study of Screening for Prostate Cancer (ERSPC) Rotterdam. The screening-effect models were included in the model. For each model the predictions of prostate cancer mortality reduction were calculated. The study compared 4 screening-effect models, which are versions of the stage-shift model or the cure model.

resultsThe stage-shift models predicted, after a follow-up of 9 years, reductions in prostate cancer mortality varying from 38% to 63% for ERSPC-Rotterdam compared with a 27% reduction observed in the ERSPC. The cure models predicted reductions in prostate cancer mortality varying from 21% to 27%.

conclusionsThe differences in predicted mortality reductions show the importance of validating models to observed trial mortality data. The stage-shift models considerably overestimated the mortality reduction. Therefore, the stage-shift models should be used with care, especially when modeling the effect of screening for cancers with long lead times, such as prostate cancer.

Indexed as

Disease ProgressionModels, TheoreticalEarly DiagnosisHumansMaleMass ScreeningProstatic Neoplasms

Identifiers

PMID21406620
PMCPMC4789305
OpenAlexW2053960955

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.