title: The performance of risk prediction models.
abstract: For medical decision making and patient information, predictions of future status variables play an important role. Risk prediction models can be derived with many different statistical approaches. To compare them, measures of predictive performance are derived from ROC methodology and from probability forecasting theory. These tools can be applied to assess single markers, multivariable regression models and complex model selection algorithms. This article provides a systematic review of the modern way of assessing risk prediction models. Particular attention is put on proper benchmarks and resampling techniques that are important for the interpretation of measured performance. All methods are illustrated with data from a clinical study in head and neck cancer patients.
authors.affiliation: Department of Biostatistics, Øster Farimagsgade 5 opg. B, Postboks 2099, 1014 København. tag@biostat.ku.dk
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Gerds
authors.forename: Tianxi
authors.initials: T
authors.lastname: Cai
authors.forename: Martin
authors.initials: M
authors.lastname: Schumacher
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D015982:Bias; D003658:Decision Making, Computer-Assisted; D006258:Head and Neck Neoplasms / Q000175:diagnosis; D006801:Humans; D015233:Models, Statistical; D011379:Prognosis; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods