English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Although randomised controlled trials are the preferred basis for policy decisions on cancer screening, it remains difficult to assess all downstream effects of screening, particularly when screening options other than those in the specific trial design are being considered. Simulation models of the natural history of disease can play a role in quantifying harms and benefits of cancer screening scenarios. Recently, the US Preventive Services Task Force issued a C-recommendation on screening for prostate cancer for men aged 55-69 years, implying at least moderate certainty that the benefit is small. However, modelling based on data from the European Randomized study of Screening for Prostate Cancer, which included quality-of-life estimates, showed that the ratio between benefits and harms is better, and likely to be reasonable, for men screened between the ages of 55 and 63 years (i.e. by using an earlier stopping age than applied in the trial setting). This commentary article considers the importance of simulation modelling in the decision-making process for (prostate) cancer screening. The paper also explores whether the recently published Cluster Randomized Trial of PSA Testing for Prostate Cancer, a trial of a single prostate specific antigen (PSA) testing intervention in the UK, changes the evidence for regular PSA testing for men aged 55-63 years by replicating the trial using a simulation model.

The role of modelling in the policy decision making process for cancer screening: example of prostate specific antigen screening