Statistical Primer: Developing and validating a risk prediction model

Journal article

A risk prediction model is a mathematical equation that uses patient risk factor data to estimate the probability of a patient experiencing a healthcare outcome. Risk prediction models are widely studied in the cardiothoracic surgical literature with most developed using logistic regression. For a risk prediction model to be useful, it must have adequate discrimination, calibration, face validity and clinical usefulness. A basic understanding of the advantages and potential limitations of risk prediction models is vital before applying them in clinical practice. This article provides a brief overview for the clinician on the various issues to be considered when developing or validating a risk prediction model. An example of how to develop a simple model is also included.

Authors: Grant, S; Collins, G; Nashef, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Oxford University Press
• Journal: European Journal of Cardio-Thoracic Surgery
• Publication date: 2018-05-07
• Acceptance date: 2018-04-02
• DOI: 10.1093/ejcts/ezy180
• EISSN: 1873-734X
• ISSN: 1010-7940
• Pmid: 29741602
• Language: English
• Keywords: EuroSCORE II; calibration; risk prediction; receiver operating characteristic; EuroSCORE; surgical mortality; risk assessment