Multiple imputation by chained equations: what is it and how does it work?

Melissa J Azur, Elizabeth A Stuart, Constantine Frangakis, Philip J Leaf
Author Information
  1. Melissa J Azur: Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. mazur@mathematica-mpr.com

Abstract

Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided.

Keywords

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Grants

  1. R24 HD042854/NICHD NIH HHS
  2. K25 MH083846/NIMH NIH HHS
  3. R01 MH075828/NIMH NIH HHS
  4. 1R01MH075828‐01A1/NIMH NIH HHS
  5. R01 MH075828-01A1/NIMH NIH HHS

MeSH Term

Epidemiologic Methods
Humans
Models, Statistical
Research Design

Word Cloud

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