Towards better clinical prediction models: seven steps for development and an ABCD for validation

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ID: 291948
2014
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Abstract
Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or an event in the future course of disease (prognosis) for individual patients. Although publications that present and evaluate such models are becoming more frequent, the methodology is often suboptimal. We propose that seven steps should be considered in developing prediction models: (i) consideration of the research question and initial data inspection; (ii) coding of predictors; (iii) model specification; (iv) model estimation; (v) evaluation of model performance; (vi) internal validation; and (vii) model presentation. The validity of a prediction model is ideally assessed in fully independent data, where we propose four key measures to evaluate model performance: calibration-in-the-large, or the model intercept (A); calibration slope (B); discrimination, with a concordance statistic (C); and clinical usefulness, with decision-curve analysis (D). As an application, we develop and validate prediction models for 30-day mortality in patients with an acute myocardial infarction. This illustrates the usefulness of the proposed framework to strengthen the methodological rigour and quality for prediction models in cardiovascular research.
Reference Key
openalex_W2154286581 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ewout W. Steyerberg, Yvonne Vergouwe
Journal european heart journal
Year 2014
DOI
10.1093/eurheartj/ehu207
URL
https://doi.org/10.1093/eurheartj/ehu207
Keywords Keywords not found

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