title: Predictive accuracy and explained variation.
abstract: Measures of the predictive accuracy of regression models quantify the extent to which covariates determine an individual outcome. Explained variation measures the relative gains in predictive accuracy when prediction based on covariates replaces unconditional prediction. A unified concept of predictive accuracy and explained variation based on the absolute prediction error is presented for models with continuous, binary, polytomous and survival outcomes. The measures are given both in a model-based formulation and in a formulation directly contrasting observed and expected outcomes. Various aspects of application are demonstrated by examples from three forms of regression models. It is emphasized that the likely degree of absolute or relative predictive accuracy often is low even if there are highly significant and relatively strong covariates.
authors.affiliation: Section of Clinical Biometrics, Department of Medical Computer Sciences, Vienna University, Spitalgasse 23, A-1090 Vienna, Austria. michael.scheper@akh-wien.ac.at
authors.forename: Michael
authors.initials: M
authors.lastname: Schemper
mesh_terms: D001317:Austria; D001724:Birth Weight; D002986:Clinical Trials as Topic; D005260:Female; D005544:Forecasting / Q000379:methods*; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D008429:Maternal Welfare; D017063:Outcome Assessment, Health Care; D016012:Poisson Distribution; D011247:Pregnancy; D016016:Proportional Hazards Models; D011471:Prostatic Neoplasms / Q000401:mortality; D015203:Reproducibility of Results; D016019:Survival Analysis