BIMAM—a tool for imputing variables missing across datasets using a Bayesian imputation and analysis model

ABSTRACT: Abstract

Motivation

Combination of multiple datasets is routine in modern epidemiology. However, studies may have measured different sets of variables; this is often inefficiently dealt with by excluding studies or dropping variables. Multilevel multiple imputation methods to impute these ‘systematically’ missing data (as opposed to ‘sporadically’ missing data within a study) are available, but problems may arise when many random effects are needed to allow for heterogeneity across studies. We show that the Bayesian IMputation and Analysis Model (BIMAM) implemented in our tool works well in this situation.

General features

BIMAM performs imputation and analysis simultaneously. It imputes both binary and continuous systematically and sporadically missing data, and analyses

SUBMITTER: Elfadaly F 

PROVIDER: S-EPMC8580266 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature