Real-time imputation of missing predictor values improved the application of prediction models in daily practice.

Autor: Nijman SWJ; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands. Electronic address: s.w.j.nijman@umcutrecht.nl., Groenhof TKJ; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands., Hoogland J; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands., Bots ML; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands., Brandjes M; LogiqCare, Ortec B.V., Zoetermeer, the Netherlands., Jacobs JJL; LogiqCare, Ortec B.V., Zoetermeer, the Netherlands., Asselbergs FW; Division Heart & Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; Health Data Research UK, Institute of Health Informatics, University College London, London, UK., Moons KGM; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands., Debray TPA; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.
Jazyk: angličtina
Zdroj: Journal of clinical epidemiology [J Clin Epidemiol] 2021 Jun; Vol. 134, pp. 22-34. Date of Electronic Publication: 2021 Jan 19.
DOI: 10.1016/j.jclinepi.2021.01.003
Abstrakt: Objectives: In clinical practice, many prediction models cannot be used when predictor values are missing. We, therefore, propose and evaluate methods for real-time imputation.
Study Design and Setting: We describe (i) mean imputation (where missing values are replaced by the sample mean), (ii) joint modeling imputation (JMI, where we use a multivariate normal approximation to generate patient-specific imputations), and (iii) conditional modeling imputation (CMI, where a multivariable imputation model is derived for each predictor from a population). We compared these methods in a case study evaluating the root mean squared error (RMSE) and coverage of the 95% confidence intervals (i.e., the proportion of confidence intervals that contain the true predictor value) of imputed predictor values.
Results: -RMSE was lowest when adopting JMI or CMI, although imputation of individual predictors did not always lead to substantial improvements as compared to mean imputation. JMI and CMI appeared particularly useful when the values of multiple predictors of the model were missing. Coverage reached the nominal level (i.e., 95%) for both CMI and JMI.
Conclusion: Multiple imputations using either CMI or JMI is recommended when dealing with missing predictor values in real-time settings.
(Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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