Joint modelling of PSA dynamics and prostate cancer risks: A population-based study in men without prior prostate...

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.