Penalized Regression and Multiple Imputation: A Simple Aggregation Rule That Works Surprisingly Well

• Activity Number: 286 - Missing Data Methods
• Type: Contributed
• Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Biometrics Section
• Author(s): Ryan A Peterson*
• Companies: University of Colorado
• Keywords: Missing data; Lasso; Minimax concave penalty; Elastic net; Regularization

Abstract

Early in the course of the pandemic in Colorado in 2020, 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). In this work, 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.