A natural history model for planning prostate cancer testing: calibration and validation using Swedish registry data
AbstractRecent prostate cancer screening trials have given conflicting results and it is unclear how to reduce prostate cancer mortality while minimising overdiagnosis and overtreatment. Prostate cancer testing is a partially observable process, and planning for testing requires either extrapolation from randomised controlled trials or, more flexibly, modelling of the cancer natural history.An existing US prostate cancer natural history model (Gulati et al, Biostatistics 2010;11:707-719) did not model for differences in survival between Gleason 6 and 7 cancers and predicted too few Gleason 7 cancers for contemporary Sweden. We re-implemented and re-calibrated the US model to Sweden. We extended the model to more finely describe the disease states, their time to biopsy-detectable cancer and prostate cancer survival. We first calibrated the model to the incidence rate ratio observed in the European Randomised Study of Screening for Prostate Cancer (ERSPC) together with age-specific cancer staging observed in the Stockholm PSA (prostate-specific antigen) and Biopsy Register; we then calibrated age-specific survival by disease states under contemporary testing and treatment using the Swedish National Prostate Cancer Register.After calibration, we were able to closely match observed prostate cancer incidence trends in Sweden. Assuming that patients detected at an earlier stage by screening receive a commensurate survival improvement, we find that the calibrated model replicates the observed mortality reduction in a simulation of ERSPC.Using the resulting model, we predicted incidence and mortality following the introduction of regular testing. Compared with a model of the current testing pattern, organised 8 yearly testing for men aged 55–69 years was predicted to reduce prostate cancer incidence by 0.11% with no increase in the mortality rate. The model is open source and suitable for planning for effective prostate cancer screening into the future.Author summaryA naïve perspective is that cancer screening is simple: people are screened, some cancers are detected early, and cancer mortality rates decline. However, the mathematics for screening becomes difficult quickly, it is hard to infer causation from observational data, and even large randomised screening studies provide limited evidence. Simulations are therefore important for planning cancer screening.We found an older US prostate cancer natural history model to be poorly suited for contemporary Sweden. We therefore re-implemented and re-calibrated the US model using data from Swedish registries.Our revised model, the Stockholm “Prostata” model, provides predictions similar to those observed in the detailed Swedish registers on prostate cancer incidence and mortality. By modelling the mechanisms of the screening effect, we can predict the benefits and harms under a range of screening interventions.
