Abstract
New predictors of cardiovascular outcomes are widely sought in research settings, and predictive tests are commonly recommended for routine use in cardiovascular clinical care. A number of multivariable scoring systems are in use around the world for assessment of a patient’s risk. While such scoring systems are often recommended for clinical use in medical practice guidelines, their actual use in medical care falls short of recommendations. Limitations in the predictive capacity of existing predictive models are recognized, including lack of predictive accuracy, lack of ability to separate those who develop events from those who do not, and risks and costs of the testing modalities. Biomarker research is actively developing new testing strategies trying to improve upon current approaches, but it is often unclear how to assess the incremental prognostic information that a new test provides. In this report, we discuss the statistical approaches that can be used to evaluate additive predictive value of new tests. We also consider clinical research examples to put this information into a practical context.
Keywords: Statistics, biomarkers, prediction, cardiovascular risk
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