A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative
Evans, Michael D
Tronieri, Jena S
Callahan, Tiffany J
Chan, Lauren E
Bramante, Carolyn T
Buse, John B
Moffitt, Richard A
Johnson, Steven G
Raymond Shao, Yu
Robinson, Peter N
Huling, Jared D
Wilkins, Kenneth J
UMass Chan AffiliationsCenter for Clinical and Translational Science
Document TypeJournal Article
KeywordsCOVID-19 severity assessment
MetadataShow full item record
AbstractHealthcare 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.
SourceCasiraghi E, Wong R, Hall M, Coleman B, Notaro M, Evans MD, Tronieri JS, Blau H, Laraway B, Callahan TJ, Chan LE, Bramante CT, Buse JB, Moffitt RA, Stürmer T, Johnson SG, Raymond Shao Y, Reese J, Robinson PN, Paccanaro A, Valentini G, Huling JD, Wilkins KJ; N3C Consortium. A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative. J Biomed Inform. 2023 Mar;139:104295. doi: 10.1016/j.jbi.2023.104295. Epub 2023 Jan 27. PMID: 36716983.
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/52127
Funding and AcknowledgementsThe UMass Center for Clinical and Translational Science (UMCCTS), UL1TR001453, provided data for this study.
Rights© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
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Except where otherwise noted, this item's license is described as © 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).