ArticlePloS one2026
Joint modelling of PSA dynamics and prostate cancer risks: A population-based study in men without prior prostate cancer.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While the prostate-specific antigen (PSA) test is a widely used prostate cancer screening tool, its application remains controversial. Opportunistic PSA testing generates complex data in which testing intensities, PSA levels, and prostate cancer diagnosis are interdependent. Conventional analyses rarely model these processes jointly. The objective of this study was to develop a population-based joint model to analyse PSA dynamics, retesting patterns, and prostate cancer risk. We used the Stockholm Prostate Cancer Diagnostics Register to identify 506,761 men with at least one PSA test between 2003 and 2020. We fitted a joint model linking three components: a linear mixed-effects submodel for PSA over age, and two proportional hazards submodels for time to next PSA test and time to prostate cancer diagnosis. PSA increased nonlinearly with age, with substantial between-person heterogeneity and increasing unexplained variation with increasing age. Estimates from models fitted to each time-to-event process separately were attenuated relative to the joint model: hazard ratios per doubling of PSA were 1.61 (95% CI: 1.59-1.62; P < .001) versus 2.01 (95% CI: 1.99-2.02; P < .001) for prostate cancer diagnosis, respectively, and 1.068 (95% CI: 1.066-1.070; P < .001) versus 1.163 (95% CI: 1.161-1.165; P < .001) for retesting, respectively. This pattern is consistent with informative observation bias: men with higher PSA values are tested more frequently, so that part of the association between PSA and the event is absorbed by the unmodelled observation mechanism when the processes are analysed separately. Jointly modelling PSA, testing intensity, and diagnosis through shared random effects accounts for this dependence. As a limitation, the study is primarily limited by its observational nature and a lack of data on non-cancer factors that can elevate PSA, such as urinary tract infections or lower urinary tract symptoms. In conclusion, jointly modelling PSA dynamics and testing behaviour corrects for the informative observation bias inherent in opportunistic testing. This approach yields more accurate population estimates compared to traditional isolated models. Our findings suggest that PSA dynamics may be clinically informative and that screening models should jointly incorporate testing history and PSA trajectories to improve precision.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.