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Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported

Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S.; Riley, Richard D.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported Thumbnail


Authors

Rebecca Whittle

George Peat

Gary S. Collins

Richard D. Riley



Abstract

Objective
Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use.

Methods
A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error, and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risks.

Results
Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorized as high risk of error; however, this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured.

Conclusion
Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions.

Journal Article Type Review
Acceptance Date May 14, 2018
Online Publication Date Jun 29, 2018
Publication Date Oct 1, 2018
Publicly Available Date Mar 28, 2024
Journal Journal of Clinical Epidemiology
Print ISSN 0895-4356
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 102
Pages 38-49
DOI https://doi.org/10.1016/j.jclinepi.2018.05.008
Keywords Prediction models; prediction; prognosis; diagnosis; measurement error; error
Publisher URL https://www.sciencedirect.com/science/article/pii/S0895435618300374

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