New Model Shows How PSA Tests Drive Repeat Testing and Cancer Diagnosis
Researchers analyzing over half a million men found that PSA test results trigger a feedback loop: doubling PSA levels doubles cancer diagnosis risk but also increases retesting by 16%. The finding could reshape screening strategies and help health systems predict testing patterns and resource needs.
Originaltitel: Joint modelling of PSA dynamics and prostate cancer risks: A population-based study
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. In the joint model, doubling of the total PSA values was associated with a hazard ratio (HR) of 2.01 (95% CI: 1.99–2.02; P < .001) for diagnosis and 1.163 (95% CI: 1.161–1.165; P < .001) for retesting. These hazard ratios were significantly stronger than estimates obtained when modelling these processes separately. 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. Furthermore, the model does not explicitly account for PSA trajectory changes after cancer onset. 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 and personalised risk predictions 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.