Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls | Synapse
June 29, 2009Open Access
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls
Key Points
The central aim is to explore the effective use of multiple imputation to handle missing data in research.
Review of statistical literature on multiple imputation.
Analysis of case studies in epidemiological and clinical settings.
Guidelines for appropriate reporting and application of the method.
Multiple imputation can improve accuracy in data analysis.
Proper reporting enhances credibility in research findings.
Common pitfalls include incorrect assumptions about missing data.
Abstract
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