Literature DB >> 21499542

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

Melissa J Azur1, Elizabeth A Stuart, Constantine Frangakis, Philip J Leaf.   

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.

Entities:  

Keywords:  analyze; missing data; multiple imputation

Mesh:

Year:  2011        PMID: 21499542      PMCID: PMC3074241          DOI: 10.1002/mpr.329

Source DB:  PubMed          Journal:  Int J Methods Psychiatr Res        ISSN: 1049-8931            Impact factor:   4.035


  19 in total

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Authors:  J L Schafer
Journal:  Stat Methods Med Res       Date:  1999-03       Impact factor: 3.021

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Authors:  John W Graham; Allison E Olchowski; Tamika D Gilreath
Journal:  Prev Sci       Date:  2007-06-05

6.  Multiple imputation of discrete and continuous data by fully conditional specification.

Authors:  Stef van Buuren
Journal:  Stat Methods Med Res       Date:  2007-06       Impact factor: 3.021

7.  Evaluation of software for multiple imputation of semi-continuous data.

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Journal:  Stat Methods Med Res       Date:  2007-06       Impact factor: 3.021

8.  Incomplete hierarchical data.

Authors:  Caroline Beunckens; Geert Molenberghs; Herbert Thijs; Geert Verbeke
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9.  Multiple imputation in a large-scale complex survey: a practical guide.

Authors:  Y He; A M Zaslavsky; M B Landrum; D P Harrington; P Catalano
Journal:  Stat Methods Med Res       Date:  2009-08-04       Impact factor: 3.021

10.  Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response.

Authors:  Recai M Yucel
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2008-07-13       Impact factor: 4.226

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  575 in total

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9.  Filling the gap in functional trait databases: use of ecological hypotheses to replace missing data.

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