Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them

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Bibliographische Detailangaben
Veröffentlicht in:	BMJ 2009-06, Vol.338 (7713), p.157-160
Hauptverfasser:	Sterne, Jonathan A C, White, Ian R, Carlin, John B, Spratt, Michael, Royston, Patrick, Kenward, Michael G, Wood, Angela M, Carpenter, James R
Format:	Artikel
Sprache:	eng
Schlagworte:	Bias Biological and medical sciences Biomedical Research - standards Biomedical Research - statistics & numerical data Blood pressure Body mass index Cardiovascular disease Cholesterols Clinical research Coronary artery disease Data analysis Data Collection - standards Data Collection - statistics & numerical data Data imputation Datasets Epidemiology General aspects Headache (Including Migraine) Health outcomes Heart Hypertension Medical journals Medical literature Medical research Medical sciences Miscellaneous Missing data Modeling Multiple imputation Pain (Neurology) Public health. Hygiene Public health. Hygiene-occupational medicine Random Allocation Research Design Research methods RESEARCH METHODS & REPORTING Statistical methods Systolic blood pressure Validity Variables
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Beschreibung
Zusammenfassung:	Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them
ISSN:	0959-8138 0959-8146 0959-535X 1468-5833 1756-1833
DOI:	10.1136/bmj.b2393