ABSTRACT
Before a risk prediction model can be recommended for clinical or public health applications, one needs to assess how good the predictions are. This chapter considers various criteria for assessing the performance of a risk model. Although the criteria apply to absolute risk, many are also useful for other types of risk, such as the pure risk of an event or the risk of having prevalent screen-detectable disease. Unless otherwise stated, we assume that we have developed a risk model on "training data" and assess the performance of the model on independent "test" or "validation" data. This approach, termed "external validation", provides a more rigorous assessment of the model than testing the model on the training data ("internal validation"), even though cross-validation techniques are available to reduce the "over-optimism" bias that can result from testing the model on the training data.
