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A review of statistical updating methods for clinical prediction models

Su, Ting-Li and Jaki, Thomas Friedrich and Hickey, Graeme and Buchan, Iain and Sperrin, Matthew (2018) A review of statistical updating methods for clinical prediction models. Statistical Methods in Medical Research, 27 (1). pp. 185-197. ISSN 0962-2802

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    Abstract

    A clinical prediction model (CPM) is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new CPM for each population and context, however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing CPMs already developed for use in similar contexts or populations. In addition, CPMs commonly become miscalibrated over time, and need replacing or updating. In this paper we review a range of approaches for re-using and updating CPMs; these fall in three main categories: simple coefficient updating; combining multiple previous CPMs 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 UK: 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 CPMs to a new population or context, and these should be implemented rather than developing a new CPM from scratch, using a breadth of complementary statistical methods.

    Item Type: Journal Article
    Journal or Publication Title: Statistical Methods in Medical Research
    Additional Information: The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 27 (1), 2018, © SAGE Publications Ltd, 2018 by SAGE Publications Ltd at the Statistical Methods in Medical Research page: http://journals.sagepub.com/home/smm on SAGE Journals Online: http://journals.sagepub.com/
    Uncontrolled Keywords: clinical prediction model ; model updating ; model validation ; model recalibration ; risk score
    Subjects:
    Departments: Faculty of Science and Technology > Mathematics and Statistics
    ID Code: 77528
    Deposited By: ep_importer_pure
    Deposited On: 07 Jan 2016 11:00
    Refereed?: Yes
    Published?: Published
    Last Modified: 24 Sep 2018 00:43
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/77528

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