A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

ABSTRACT: 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 eval

SUBMITTER: Casiraghi E 

PROVIDER: S-EPMC10683778 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature