Which Risk Stratification Algorithms or Guidelines Delineate Risk Accurately, and in a Clinically Meaningful Way?
We reviewed published studies which examined the ability of models (or algorithms, or guidelines) which used family history information to accurately predict individual risk of breast, ovarian, colorectal, or prostate cancer. To be eligible, the model had to include systematic collection of specified family history information, either alone or with other personal or clinical information which would be available for all patients and routinely available to a primary care practitioner. We examined the performance of models in relation to populations not selected for known or suspected high risk of cancer. Model performance was assessed by predictive accuracy, in terms of calibration and discrimination. “Calibration” is a model’s ability to correctly predict the number of observed events (incidence of cancer) in a population and is generally evaluated by its goodness-of-fit to observed events. The ratio of observed to expected cases provides an overall epidemiological assessment of how wel
