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

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 psychi...

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Published in	International journal of methods in psychiatric research Vol. 20; no. 1; pp. 40 - 49
Main Authors	Azur, Melissa J., Stuart, Elizabeth A., Frangakis, Constantine, Leaf, Philip J.
Format	Journal Article
Language	English
Published	Chichester, UK John Wiley & Sons, Ltd 01.03.2011
Subjects	analyze Computer programs Data processing Epidemiologic Methods Humans Mathematical models missing data Models, Statistical multiple imputation Research Design Reviews software multiple imputation missing data analyze
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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. Copyright © 2011 John Wiley & Sons, Ltd.
AbstractList	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. Copyright © 2011 John Wiley & Sons, Ltd . 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. Copyright © 2011 John Wiley & Sons, Ltd. 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. 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.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.
Author	Stuart, Elizabeth A. Frangakis, Constantine Azur, Melissa J. Leaf, Philip J.
AuthorAffiliation	2 Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore MA USA 1 Department of Mental Health Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA
AuthorAffiliation_xml	– name: 2 Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore MA USA – name: 1 Department of Mental Health Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA
Author_xml	– sequence: 1 givenname: Melissa J. surname: Azur fullname: Azur, Melissa J. organization: Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Maryland, Baltimore, USA – sequence: 2 givenname: Elizabeth A. surname: Stuart fullname: Stuart, Elizabeth A. organization: Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Maryland, Baltimore, USA – sequence: 3 givenname: Constantine surname: Frangakis fullname: Frangakis, Constantine organization: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, MA, Baltimore, USA – sequence: 4 givenname: Philip J. surname: Leaf fullname: Leaf, Philip J. organization: Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Maryland, Baltimore, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/21499542$$D View this record in MEDLINE/PubMed
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Some practical clarifications of multiple imputation theory publication-title: Prevention Science – volume: 16 start-page: 457 year: 2007 end-page: 492 article-title: Incomplete hierarchical data publication-title: Statistical Methods in Medical Research – volume: 27 start-page: 4086 year: 2008 end-page: 4093 article-title: An imputation strategy for incomplete longitudinal ordinal data publication-title: Statistics in Medicine – year: 2005 – volume: 8 start-page: 3 year: 1999 end-page: 15 article-title: Multiple imputation: a primer publication-title: Statistical Methods in Medical Research – volume: 16 start-page: 219 year: 2007 end-page: 242 article-title: Multiple imputation of discrete and continuous data by fully conditional specification publication-title: Statistical Methods in Medical Research – year: 2000 – article-title: Multiple imputation with diagnostics (mi) in R: Opening windows into the black box publication-title: Journal of Statistical Software – volume: 6 start-page: 330 year: 2001 end-page: 351 article-title: A comparison of inclusive and restrictive strategies in modern missing data procedures publication-title: Psychological Methods – year: 2009b – volume: 26 start-page: 3057 year: 2007 end-page: 3077 article-title: Multiple imputation: review of theory, implementation and software publication-title: Statistics in Medicine – volume: 171 start-page: 624 year: 2010 end-page: 632 article-title: Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation publication-title: American Journal of Epidemiology – volume: 57 start-page: 273 year: 2008 end-page: 291 article-title: Diagnostics for multivariate imputations publication-title: Journal of the Royal Statistical Society: Series C (Applied Statistics) – volume: 142 start-page: 1255 year: 1995 end-page: 1264 article-title: A critical look at methods for handling missing covariates in epidemiologic regression analyses publication-title: American Journal of Epidemiology – volume: 169 start-page: 1133 year: 2009 end-page: 1139 article-title: Practical imputation with large datasets: a case study of the Children's Mental Health Initiative publication-title: American Journal of Epidemiology – volume: 366 start-page: 2389 year: 2008 end-page: 2403 article-title: Multiple imputation inference for multivariate multilevel continuous data with ignorable non‐response publication-title: Philosophical Transactions of the Royal Society A – volume: 57 start-page: 19 year: 2003 end-page: 35 article-title: Multiple imputation in multivariate problems when the imputation and analysis models differ publication-title: Statistica Neerlandica – volume: 61 start-page: 79 year: 2007 end-page: 90 article-title: Much ado about nothing: a comparison of missing data methods and software to fit incomplete data regression models publication-title: The American Statistician – volume: 10 start-page: 80 year: 2003 end-page: 100 article-title: Adding missing‐data relevant variables to FIML‐based structural equation models publication-title: Structural Equation Modeling – volume: 28 start-page: 3657 year: 2009 end-page: 3669 article-title: Missing values in longitudinal dietary data: a multiple imputation approach based on a fully conditional specification publication-title: Statistics in Medicine – volume: 5 start-page: 3 year: 2002 end-page: 20 article-title: Overview of the national evaluation of the comprehensive community mental health services for children and their families program publication-title: Children's Services: Social Policy, Research, and Practice – volume: 5 start-page: 188 year: 2005 end-page: 201 article-title: Multiple imputation of missing values – update publication-title: The Stata Journal – year: 2002 – year: 2008 – year: 2006 – volume: 9 start-page: 252 year: 2009 end-page: 264 article-title: Multiple imputation of missing values: new features for mim publication-title: The Stata Journal – volume: 7 start-page: 147 year: 2002 end-page: 177 article-title: Missing data: our view of the state of the art publication-title: Psychological Methods – volume: 60 start-page: 549 year: 2009 end-page: 576 article-title: Missing data analysis: making it work in the real world publication-title: Annual Review of Psychology – year: 2009a – article-title: MICE: Multivariate Imputation by Chained Equations in R publication-title: Journal of Statistical Software – volume: 101 start-page: 924 year: 2006 end-page: 933 article-title: Multiple imputation of missing income data in the National Health Interview Survey publication-title: Journal of the American Statistical Association – volume: 59 start-page: 1092 year: 2006 end-page: 1101 article-title: Using the outcome for imputation of missing predictor values was preferred publication-title: Journal of Clinical Epidemiology – volume: 27 start-page: 85 year: 2001 end-page: 95 article-title: A multivariate technique for multiply imputing missing values using a sequence of regression models publication-title: Survey Methodology – start-page: 1 year: 2009 end-page: 18 article-title: Multiple imputation in a large‐scale complex survey: a practical guide publication-title: Statistical Methods in Medical Research – volume: 16 start-page: 243 year: 2007 end-page: 258 article-title: Evaluation of software for multiple imputation of semi‐continuous data publication-title: Statistical Methods in Medical Research – year: 1999 – ident: e_1_2_10_39_1 doi: 10.1177/0962280206074463 – volume-title: WinMICE User's Manual for WinMICE Prototype Version 0.1 year: 2005 ident: e_1_2_10_15_1 – ident: e_1_2_10_14_1 doi: 10.1198/000313007X172556 – ident: e_1_2_10_16_1 doi: 10.1093/aje/kwp425 – ident: e_1_2_10_38_1 article-title: Multiple imputation with diagnostics (mi) in R: Opening windows into the black box publication-title: Journal of Statistical Software – ident: e_1_2_10_20_1 doi: 10.1016/j.jclinepi.2006.01.009 – ident: e_1_2_10_3_1 doi: 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Snippet	Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE...
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SubjectTerms	analyze Computer programs Data processing Epidemiologic Methods Humans Mathematical models missing data Models, Statistical multiple imputation Research Design Reviews software
Title	Multiple imputation by chained equations: what is it and how does it work?
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