BIMAM—a tool for imputing variables missing across datasets using a Bayesian imputation and analysis model. (6th September 2021)

• Record Type: Journal Article
• Authors: Elfadaly, Fadlalla G; Adamson, Alex; Patel, Jaymini; Potts, Laura; Potts, James; Blangiardo, Marta; Thompson, John; Minelli, Cosetta
• Is Part Of: International journal of epidemiology. Volume 50:Number 5(2021)
• Page Start: 1419
• Page End: 1425
• Publication Date: 2021-09-06
• Subjects: Multiple imputation methods -- systematically missing data -- Bayesian methods -- Bayesian hierarchical models -- R Shiny application; Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyab177
• Languages: English
• ISSNs: 0300-5771

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 binary and continuous outcomes. BIMAM is a user-friendly, freely available tool that does not require knowledge of Bayesian methods. BIMAM is an R Shiny application. It is downloadable to a local machine and it automatically installs the required freely available packages (R packages, including R2MultiBUGS and MultiBUGS). Availability: BIMAM is available at [www.alecstudy.org/bimam ].