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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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -