RT info:eu-repo/semantics/article
T1 A method for comparing multiple imputation techniques : a case study on  the U.S. national COVID cohort collaborative
A1 Casiraghi, Elena
A1 Wong, Rachel
A1 Hall, Margaret
A1 Coleman, Ben
A1 Notaro, Marco
A1 Evans, Michael D.
A1 Tronieri, Jena S.
A1 Blau, Hannah
A1 Laraway, Bryan
A1 Callahan, Tiffany J.
A1 Chan, Lauren E.
A1 Bramante, Carolyn T.
A1 Buse, John B.
A1 Moffitt, Richard A.
A1 Stürmer, Til
A1 Johnson, Steven G.
A1 Shao, Yu Raymond
A1 Reese, Justin
A1 Robinson, Peter N.
A1 Paccanaro, Alberto
A1 Valentini, Giorgio
A1 Huling, Jared D.
A1 Wilkins, Kenneth J.
A1 N3C Consortium
A2 Universidad Católica Nuestra Señora de la Asunción
A2 Cámara Paraguaya de Exportadores y Comercializadores de Cereales y Oleaginosas
A2 Centro de Ingeniería para la InvestigaciónDesarrollo e Innovación Tecnológica
AB Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm’s parameters and data-related modeling choices are also both crucial and challenging.In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters.Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.
PB Elsevier
SN 1532-0464
YR 2023
FD 2023-01-27
LK http://hdl.handle.net/20.500.14066/4735
UL http://hdl.handle.net/20.500.14066/4735
LA eng
NO Casiraghi, E., Wong, R., Hall, M., Coleman, B., Notaro, M., Evans, M. D., Tronieri, J. S., Blau, H., Laraway, B., Callahan, T., Chan, L. E., Bramante, C. T., Buse, J. B., Moffitt, R. A., Stürmer, T., Johnson, S. G., Shao, Y. R., Reese, J., Robinson, P. N., … Wilkins, K. J. (2023). A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative. Journal of Biomedical Informatics, 139, Artículo 104295.https://doi.org/10.1016/j.jbi.2023.104295
NO Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 03-sep-2026