A Model Too Far
Cet article évalue la performance d'un nomogramme, développé à l'aide d'un modèle de microsimulation numérique, pour prédire le risque individuel de surdiagnostic de cancer de la prostate à partir de l'âge du patient, du niveau sérique du PSA ou du score de Gleason au diagnostic
Screening for prostate cancer is a “double-edged tool” (1); it is associated with potential benefits (reduction in mortality) and potential harms (unnecessary treatment and emotional distress due to the detection of cancers that would never cause symptoms or death). Availability of good estimates of these benefits and harms is required to properly guide screening practice, both from the public health and individual patient perspectives. The central role in this process is played by randomized screening trials (2). However, it is well recognized that the generalizability of the results from randomized trials may be limited: the results may not be applicable to a population different than that included in the trial, and the results strictly apply only to the specific intervention tested in the trial, not to possible modifications of it. In the screening context, statistical and biological modeling approaches have the potential to increase the generalizability of randomized trial results.
In this issue of the Journal, Gulati et al. (3) use a microsimulation model for prediction of an individual’s risk that his cancer has been overdiagnosed, given that he has a biopsied prostate-specific antigen (PSA) screen-detected prostate cancer. An overdiagnosed cancer, as defined by Gulati et al., is one that would not …
Journal of the National Cancer Institute , éditorial en libre accès, 2014