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  • SuJakiHickeyBuchanSperrinDec15

    Rights statement: 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/

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A review of statistical updating methods for clinical prediction models

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A review of statistical updating methods for clinical prediction models. / Su, Ting-Li; Jaki, Thomas Friedrich; Hickey, Graeme et al.

In: Statistical Methods in Medical Research, Vol. 27, No. 1, 01.01.2018, p. 185-197.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Su, T-L, Jaki, TF, Hickey, G, Buchan, I & Sperrin, M 2018, 'A review of statistical updating methods for clinical prediction models', Statistical Methods in Medical Research, vol. 27, no. 1, pp. 185-197. https://doi.org/10.1177/0962280215626466

APA

Su, T-L., Jaki, T. F., Hickey, G., Buchan, I., & Sperrin, M. (2018). A review of statistical updating methods for clinical prediction models. Statistical Methods in Medical Research, 27(1), 185-197. https://doi.org/10.1177/0962280215626466

Vancouver

Su T-L, Jaki TF, Hickey G, Buchan I, Sperrin M. A review of statistical updating methods for clinical prediction models. Statistical Methods in Medical Research. 2018 Jan 1;27(1):185-197. Epub 2016 Jul 26. doi: 10.1177/0962280215626466

Author

Su, Ting-Li ; Jaki, Thomas Friedrich ; Hickey, Graeme et al. / A review of statistical updating methods for clinical prediction models. In: Statistical Methods in Medical Research. 2018 ; Vol. 27, No. 1. pp. 185-197.

Bibtex

@article{271f87c27c18451d9eba675abcbe9ba2,
title = "A review of statistical updating methods for clinical prediction models",
abstract = "A clinical prediction model (CPM) is a tool for predicting healthcare outcomes, usually within aspecific 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.",
keywords = "clinical prediction model, model updating, model validation, model recalibration, risk score",
author = "Ting-Li Su and Jaki, {Thomas Friedrich} and Graeme Hickey and Iain Buchan and Matthew Sperrin",
note = "The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 27 (1), 2018, {\textcopyright} 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/ ",
year = "2018",
month = jan,
day = "1",
doi = "10.1177/0962280215626466",
language = "English",
volume = "27",
pages = "185--197",
journal = "Statistical Methods in Medical Research",
issn = "0962-2802",
publisher = "SAGE Publications Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - A review of statistical updating methods for clinical prediction models

AU - Su, Ting-Li

AU - Jaki, Thomas Friedrich

AU - Hickey, Graeme

AU - Buchan, Iain

AU - Sperrin, Matthew

N1 - 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/

PY - 2018/1/1

Y1 - 2018/1/1

N2 - A clinical prediction model (CPM) is a tool for predicting healthcare outcomes, usually within aspecific 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.

AB - A clinical prediction model (CPM) is a tool for predicting healthcare outcomes, usually within aspecific 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.

KW - clinical prediction model

KW - model updating

KW - model validation

KW - model recalibration

KW - risk score

U2 - 10.1177/0962280215626466

DO - 10.1177/0962280215626466

M3 - Journal article

VL - 27

SP - 185

EP - 197

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

IS - 1

ER -