Titlebook: Clinical Prediction Models; A Practical Approach Ewout W. Steyerberg Book 2019Latest edition Springer Nature Switzerland AG 2019 An?sthesie

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Assumptions in Regression Models: Additivity and Linearitych can be assessed with interaction terms. We also consider the linearity assumption of continuous predictors in a multivariable regression model, where multiple nonlinear terms can be included to allow for nonlinear relations between predictors and outcome. Throughout we stress parsimony in strateg

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Evaluation of Performancere absolute risks, which go beyond assessments of relative risks, such as regression coefficients, odds ratios, or hazard ratios. We can distinguish apparent, internally validated, and externally validated model performance (Chap.?.). For all types of validation, we need performance criteria in line

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Evaluation of Clinical Usefulness model is useful to support medical decision-making: is the model beneficial to guide selection of subjects for screening, for diagnostic work-up, or decision-making on therapy? For such decisions, we need a cutoff for the predicted probability (“decision threshold”, or “classification cutoff”, see

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https://doi.org/10.1007/978-1-4020-2131-2ction modeling. We focus on the most relevant aspects of these models in a prediction context. All models are illustrated with case studies. In Chap. ., we will discuss aspects of choosing between alternative statistical models for the same type of outcome.