A review of statistical updating methods for clinical prediction models

Su, Ting-Li, Jaki, Thomas, Hickey, Graeme L ORCID: 0000-0002-4989-0054, Buchan, Iain ORCID: 0000-0003-3392-1650 and Sperrin, Matthew (2018) A review of statistical updating methods for clinical prediction models. STATISTICAL METHODS IN MEDICAL RESEARCH, 27 (1). pp. 185-197.

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Abstract

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods.

Item Type:	Article
Uncontrolled Keywords:	Clinical prediction model, model updating, model validation, model recalibration, risk score
Depositing User:	Symplectic Admin
Date Deposited:	13 Jan 2016 15:42
Last Modified:	16 Dec 2022 04:44
DOI:	10.1177/0962280215626466
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/2046539