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Steven W J Nijman, Artuur M. Leeuwenberg, Inés Beekers, Inge Verkouter, John J. L. Jacobs, Michiel L. Bots, Folkert Wouter Asselbergs, Karel G.M. Moons, Thomas P. A. Debray
OBJECTIVES: Missing data is a common problem during the development, evaluation, and implementation of prediction models. Although machine learning (ML) methods are often said to be capable of circumventing missing data, it is unclear how these methods are used in medical research. We aim to find out if and how well prediction model studies using machine learning report on their handling of missing data. STUDY DESIGN AND SETTING: We systematically searched the literature on published papers between 2018 and 2019 about primary studies developing and/or validating clinical prediction models using any supervised ML methodology across medical fields. From the retrieved studies information about the amount and nature (e.g. missing completely at random, potential reasons for missingness) of missing data and the way they were handled were extracted. RESULTS: We identified 152 machine learning-based clinical prediction model studies. A substantial amount of these 152 papers did not report anything on missing data (n = 56/152). A majority (n = 96/152) reported details on the handling of missing data (e.g., methods used), though many of these (n = 46/96) did not report the amount of the missingness in the data. In these 96 papers the authors only sometimes reported possible reasons for missingness (n = 7/96) and information about missing data mechanisms (n = 8/96). The most common approach for handling missing data was deletion (n = 65/96), mostly via complete-case analysis (CCA) (n = 43/96). Very few studies used multiple imputation (n = 8/96) or built-in mechanisms such as surrogate splits (n = 7/96) that directly address missing data during the development, validation, or implementation of the prediction model. CONCLUSION: Though missing values are highly common in any type of medical research and certainly in the research based on routine healthcare data, a majority of the prediction model studies using machine learning does not report sufficient information on the presence and handling of missing data. Strategies in which patient data are simply omitted are unfortunately the most often used methods, even though it is generally advised against and well known that it likely causes bias and loss of analytical power in prediction model development and in the predictive accuracy estimates. Prediction model researchers should be much more aware of alternative methodologies to address missing data.
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Nijman, S. W. J., Leeuwenberg, A. M., Beekers, I., Verkouter, I., Jacobs, J. J. L., Bots, M. L., Asselbergs, F. W., Moons, K. G., & Debray, T. P. A. (2021). Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review. Journal of Clinical Epidemiology, 142, 218–229. https://doi.org/10.1016/j.jclinepi.2021.11.023
Nijman, Steven W J, et al. "Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review." Journal of Clinical Epidemiology, vol. 142, 2021, pp. 218–229, https://doi.org/10.1016/j.jclinepi.2021.11.023.
Nijman, Steven W J, Artuur M. Leeuwenberg, Inés Beekers, Inge Verkouter, John J. L. Jacobs, Michiel L. Bots, Folkert Wouter Asselbergs, Karel G.M. Moons, and Thomas P. A. Debray. "Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review." Journal of Clinical Epidemiology 142 (2021): 218–229. https://doi.org/10.1016/j.jclinepi.2021.11.023.
Nijman S. W. J., Leeuwenberg A. M., Beekers I. [и др.] Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review // Journal of Clinical Epidemiology. — 2021. — Vol. 142. — P. 218–229. — URL: https://doi.org/10.1016/j.jclinepi.2021.11.023
@article{nijman2021missing,
title = {{Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review}},
author = {Steven W J Nijman and Artuur M. Leeuwenberg and Inés Beekers and Inge Verkouter and John J. L. Jacobs and Michiel L. Bots and Folkert Wouter Asselbergs and Karel G.M. Moons and Thomas P. A. Debray},
journal = {Journal of Clinical Epidemiology},
year = {2021},
volume = {142},
pages = {218--229},
doi = {10.1016/j.jclinepi.2021.11.023},
url = {https://doi.org/10.1016/j.jclinepi.2021.11.023}
}TY - JOUR AU - Nijman, Steven W J AU - Leeuwenberg, Artuur M. AU - Beekers, Inés AU - Verkouter, Inge AU - Jacobs, John J. L. AU - Bots, Michiel L. AU - Asselbergs, Folkert Wouter AU - Moons, Karel G.M. AU - Debray, Thomas P. A. TI - Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review JO - Journal of Clinical Epidemiology VL - 142 SP - 218 EP - 229 PY - 2021 DA - 2021/11/16 DO - 10.1016/j.jclinepi.2021.11.023 UR - https://doi.org/10.1016/j.jclinepi.2021.11.023 ER -
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