Background. Simulation models are essential tools for estimating benefits of cancer screening programs. Such models include a screening-effect model that represents how early detection by screening followed by treatment affects disease-specific survival. Two commonly used screening-effect models are the stage-shift model, where mortality benefits are explained by the shift to more favorable stages, and the cure model, where early detection enhances the chances of cure from disease. Objective. This article describes commonly used screening-effect models and analyses their predicted mortality benefit in a model for prostate cancer screening. Method. The MISCAN simulation model was used to predict the reduction of prostate cancer mortality in the European Randomized Study of Screening for Prostate Cancer (ERSPC) Rotterdam. The screening-effect models were included in the model. For each model the predictions of prostate cancer mortality reduction were calculated. The study compared 4 screening-effect models, which are versions of the stage-shift model or the cure model. Results. The stage-shift models predicted, after a follow-up of 9 years, reductions in prostate cancer mortality varying from 38% to 63% for ERSPC-Rotterdam compared with a 27% reduction observed in the ERSPC. The cure models predicted reductions in prostate cancer mortality varying from 21% to 27%. Conclusions. The differences in predicted mortality reductions show the importance of validating models to observed trial mortality data. The stage-shift models considerably overestimated the mortality reduction. Therefore, the stage-shift models should be used with care, especially when modeling the effect of screening for cancers with long lead times, such as prostate cancer.

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Medical Decision Making: an international journal
Erasmus MC: University Medical Center Rotterdam

Wever, E., Draisma, G., Heijnsdijk, E., & de Koning, H. (2011). How does early detection by screening affect disease progression?: Modeling estimated benefits in prostate cancer screening. Medical Decision Making: an international journal, 31(4), 550–558. doi:10.1177/0272989X10396717