Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments.
%0 Journal Article
%1 White2011
%A White, Ian R
%A Royston, Patrick
%A Wood, Angela M
%D 2011
%J Statistics in medicine
%K Adolescent Adult Aged CardiovascularDiseases CardiovascularDiseases:epidemiology Cholesterol Cholesterol:blood Female HDL HDL:blood Humans Lipoproteins MentalHealth MentalHealth:statistics&numericaldata MiddleAged Models MulticenterStudiesasTopic Statistical YoungAdult
%N 4
%P 377-99
%R 10.1002/sim.4067
%T Multiple imputation using chained equations: Issues and guidance for practice.
%U http://www.ncbi.nlm.nih.gov/pubmed/21225900
%V 30
%X Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments.
@article{White2011,
abstract = {Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {White, Ian R and Royston, Patrick and Wood, Angela M},
biburl = {https://www.bibsonomy.org/bibtex/2ddf34b2a1978f1ac557d0e705276d204/jepcastel},
doi = {10.1002/sim.4067},
interhash = {6a5c44fa2ee2bb648a3ed943dc2db8bd},
intrahash = {ddf34b2a1978f1ac557d0e705276d204},
issn = {1097-0258},
journal = {Statistics in medicine},
keywords = {Adolescent Adult Aged CardiovascularDiseases CardiovascularDiseases:epidemiology Cholesterol Cholesterol:blood Female HDL HDL:blood Humans Lipoproteins MentalHealth MentalHealth:statistics&numericaldata MiddleAged Models MulticenterStudiesasTopic Statistical YoungAdult},
month = {2},
note = {Dades censurades; Imputació múltiple},
number = 4,
pages = {377-99},
pmid = {21225900},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Multiple imputation using chained equations: Issues and guidance for practice.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21225900},
volume = 30,
year = 2011
}