Variable selection with multiply-imputed datasets: choosing between stacked and grouped methods.

ABSTRACT: Penalized regression methods are used in many biomedical applications for variable selection and simultaneous coefficient estimation. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors. This paper considers a general class of penalized objective functions which, by construction, force selection of the same variables across imputed datasets. By pooling objective functions across imputations, optimization is then performed jointly over all imputed datasets rather than separately for each dataset. We consider two objective function formulations that exist in the literature, which we wil

SUBMITTER: Du J 

PROVIDER: S-EPMC9838615 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature