Real-time imputation of missing predictor values improved the application of prediction models in daily practice
| Autor: | T. Katrien J. Groenhof, Michiel L. Bots, Thomas P. A. Debray, Steven W J Nijman, Menno Brandjes, Karel G.M. Moons, Jeroen Hoogland, John J.L. Jacobs, Folkert W. Asselbergs |
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| Přispěvatelé: | Epidemiology and Data Science, Cardiology |
| Rok vydání: | 2021 |
| Předmět: |
education.field_of_study
Statistics::Applications Mean squared error Epidemiology Population Multivariate normal distribution Missing data Confidence interval Nominal level 03 medical and health sciences 0302 clinical medicine Data Interpretation Statistical Statistics Humans Statistics::Methodology Computer Simulation 030212 general & internal medicine Imputation (statistics) Precision Medicine education Algorithms 030217 neurology & neurosurgery Predictive modelling Mathematics |
| Zdroj: | Journal of Clinical Epidemiology, 134, 22-34. Elsevier USA |
| ISSN: | 0895-4356 |
| DOI: | 10.1016/j.jclinepi.2021.01.003 |
| Popis: | 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 95i.e., the proportion of confidence intervals that contain the true predictor value) of imputed predictor values. Results: eRMSE 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. Ó 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/). |
| Databáze: | OpenAIRE |
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