JDS Journal of Data Science 1683-8602 1680-743X 1680-743X School of Statistics, Renmin University of China JDS995 10.6339/21-JDS995 Computing in Data Science A Simple Aggregation Rule for Penalized Regression Coefficients after Multiple Imputation PetersonRyan A.ryan.a.peterson@cuanschutz.edu1∗ Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Denver Anschutz Medical Campus, Aurora, Colorado, USA Email: ryan.a.peterson@cuanschutz.edu. 20212812021 191114 Supplementary Material

A script to reproduce simulations under varied parameters has been provided as supplemental material online, along with an appendix containing additional tables and figures pertaining to the simulations described herein.

 72020 102020 2021 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2021 Open access article under the CC BY license.

Early in the course of the pandemic in Colorado, researchers wished to fit a sparse predictive model to intubation status for newly admitted patients. Unfortunately, the training data had considerable missingness which complicated the modeling process. I developed a quick solution to this problem: Median Aggregation of penaLized Coefficients after Multiple imputation (MALCoM). This fast, simple solution proved successful on a prospective validation set. In this manuscript, I show how MALCoM performs comparably to a popular alternative (MI-lasso), and can be implemented in more general penalized regression settings. A simulation study and application to local COVID-19 data is included.

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