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

• Published in: BMJ Vol. 338; no. 7713; pp. 157 - 160
• Main Authors: Sterne, Jonathan A C, White, Ian R, Carlin, John B, Spratt, Michael, Royston, Patrick, Kenward, Michael G, Wood, Angela M, Carpenter, James R
• Format: Journal Article
• Language: English
• Published: London British Medical Journal Publishing Group 29-06-2009; British Medical Association; BMJ Publishing Group LTD; BMJ Publishing Group; BMJ Publishing Group Ltd
• Edition: International edition
• Subjects: Bias; Biological and medical sciences; Biomedical Research - standards; Biomedical Research - statistics & numerical data; Blood pressure; Body mass index; Cardiovascular disease; Cholesterols; 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 research; Medical sciences; Miscellaneous; Missing data; Modeling; 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; Medicine; Missing data; Clinical research; Multiple; Epidemiology; Public health
• ISSN: 0959-8138; 0959-8146; 1468-5833; 1756-1833
• DOI: 10.1136/bmj.b2393

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