Missing data and multiple imputation in clinical epidemiological research.
Alma B Pedersen,Ellen M. Mikkelsen,Deirdre Cronin-Fenton,Nickolaj R. Kristensen,Tra My Pham,Lars Pedersen,Irene Petersen +6 more
TLDR
Multiple imputation is an alternative method to deal withMissing data, which accounts for the uncertainty associated with missing data, and provides unbiased and valid estimates of associations based on information from the available data.Abstract:
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.read more
Citations
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References
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The Strengthening the Reporting of Observational Studies in Epidemiology [STROBE] statement: guidelines for reporting observational studies
Erik von Elm,Douglas G. Altman,Matthias Egger,Matthias Egger,Stuart J. Pocock,Peter C Gøtzsche,Jan P. Vandenbroucke +6 more
TL;DR: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study, resulting in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.
Journal ArticleDOI
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies
E von Elm,Douglas G. Altman,Matthias Egger,Matthias Egger,Stuart J. Pocock,Peter C Gøtzsche,Jan P. Vandenbroucke +6 more
TL;DR: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study, resulting in a checklist of 22 items that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.
Journal ArticleDOI
Missing data: Our view of the state of the art.
Joseph L. Schafer,John W. Graham +1 more
TL;DR: 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.
Journal ArticleDOI
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies
E von Elm,Douglas G. Altman,Matthias Egger,Matthias Egger,Stuart J. Pocock,Peter C Gøtzsche,Jan P. Vandenbroucke +6 more
TL;DR: The STROBE Statement is a checklist of items that should be addressed in articles reporting on the 3 main study designs of analytical epidemiology: cohort, casecontrol, and cross-sectional studies; these recommendations are not prescriptions for designing or conducting studies.
Journal ArticleDOI
Multiple imputation using chained equations: Issues and guidance for practice
TL;DR: The principles of the method and how to impute categorical and quantitative variables, including skewed variables, are described and shown and the practical analysis of multiply imputed data is described, including model building and model checking.