BIMAM-a tool for imputing variables missing across datasets using a Bayesian imputation and analysis model
File(s)
Published version
Author(s)
Type
Journal Article
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].
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].
Date Issued
2021-10
Date Acceptance
2021-08-03
Citation
International Journal of Epidemiology, 2021, 50 (5), pp.1419-1425
ISSN
0300-5771
Publisher
Oxford University Press
Start Page
1419
End Page
1425
Journal / Book Title
International Journal of Epidemiology
Volume
50
Issue
5
Copyright Statement
© The Author(s) 2021. Published by Oxford University Press on behalf of the International Epidemiological Association.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000728182600005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
Bayesian hierarchical models
Bayesian methods
Life Sciences & Biomedicine
METAANALYSIS
MULTIPLE IMPUTATION
Multiple imputation methods
Public, Environmental & Occupational Health
R Shiny application
Science & Technology
systematically missing data
Publication Status
Published
Date Publish Online
2021-09-06