A natural history model for planning prostate cancer testing: Calibration and validation using Swedish registry data.

Recent 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 14% and increase prostate cancer mortality by 2%. The model is open source and suitable for planning for effective prostate cancer screening into the future.
Competing Interests: Author HG has five prostate cancer diagnostic–related patents pending: Prognostic Method for Individuals with Prostate Cancer (EP2922970B1, US20150284804A1), Method for indicating the presence or non-presence of prostate cancer (CA2871877A1, US20150317431A1, EP2922967B1, WO2013172779A3), Method for indicating a presence or non-presence of prostate cancer in individuals with particular characteristics (WO2018141828A1, US20150317431A1) and Methods and compositions for correlating genetic markers with prostate cancer risk (US9534256B2, WO2009085196A1). Thermo Fisher Scientific is licensing these patent applications and HG might receive royalties from sales related to these patent applications; author ME is named on the first four of the five patent applications. Importantly, the methods disclosed by these patent applications are not part of this research paper. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The commercial appliation with AstraZeneca does not alter our adherence to PLOS ONE policies on sharing data and materials.