Missing data and multiple imputation in clinical epidemiological research

• Published in: Clinical epidemiology Vol. 9; pp. 157 - 166
• Main Authors: Pedersen, Alma, Mikkelsen, Ellen, Cronin-Fenton, Deirdre, Kristensen, Nickolaj, Pham, Tra My, Pedersen, Lars, Petersen, Irene
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2017; Taylor & Francis Ltd; Dove Medical Press
• Subjects: Analysis; Blood pressure; Body mass index; Clinical medicine; Comorbidity; Data collection; Epidemiology; MAR; MCAR; Medical research; Methodology; Missing data; Missing observations (Statistics); MNAR; multiple imputation; Multiple imputation (Statistics); Observational studies; observational study; Patients; Registration; Researchers; Studies; Variables; Denmark; Aarhus Denmark; multiple imputation; MCAR; MNAR; missing data; MAR; observational study
• ISSN: 1179-1349; 1179-1349
• DOI: 10.2147/CLEP.S129785

Missing data are ubiquitous in clinical epidemiological research. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data are often categorized into the following three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In clinical epidemiological research, missing data are seldom MCAR. Missing data can constitute considerable challenges in the analyses and interpretation of results and can potentially weaken the validity of results and conclusions. A number of methods have been developed for dealing with missing data. These include complete-case analyses, missing indicator method, single value imputation, and sensitivity analyses incorporating worst-case and best-case scenarios. If applied under the MCAR assumption, some of these methods can provide unbiased but often less precise estimates. Multiple imputation is an alternative method to deal with missing data, which accounts for the uncertainty associated with missing data. Multiple imputation is implemented in most statistical software under the MAR assumption and provides unbiased and valid estimates of associations based on information from the available data. The method affects not only the coefficient estimates for variables with missing data but also the estimates for other variables with no missing data.