Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

Diagnostic and Prognostic Research
Lucy M BullJamie C Sergeant

Abstract

Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers. MEDLINE, Embase and Web of Science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method. The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised esti...Continue Reading

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Citations

Nov 10, 2020·Journal of the American Medical Informatics Association : JAMIA·Rose SiskGlen P Martin

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Software Mentioned

Mendeley Desktop
random
Embase
merlin
gbm R
QRISK
LibSVM
R
Ovid
merlin package

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