title: Multiple imputation using chained equations: Issues and guidance for practice.
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.
authors.affiliation: MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, U.K.. ian.white@mrc-bsu.cam.ac.uk.
authors.forename: Ian R
authors.initials: IR
authors.lastname: White
authors.forename: Patrick
authors.initials: P
authors.lastname: Royston
authors.forename: Angela M
authors.initials: AM
authors.lastname: Wood
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002318:Cardiovascular Diseases / Q000453:epidemiology; D002784:Cholesterol / Q000097:blood; D005260:Female; D006801:Humans; D008075:Lipoproteins, HDL / Q000097:blood; D008603:Mental Health / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D015337:Multicenter Studies as Topic; D055815:Young Adult
chemical_list: D008075:Lipoproteins, HDL; D002784:Cholesterol