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

Jonathan A C Sterne; Ian R. White; John B. Carlin; Michael Spratt; Patrick Royston; Michael G. Kenward; Angela Wood; James R. Carpenter

doi:10.1136/bmj.b2393

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

Keywords

Imputation (statistics)Missing dataComputer scienceData scienceData miningStatisticsEconometricsMathematicsMachine learning

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Publication Info

Year: 2009
Type: article
Volume: 338
Issue: jun29 1
Pages: b2393-b2393
Citations: 6726
Access: Closed

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APA Style

                            
                                
                                    Jonathan A C Sterne, 
                                
                                    Ian R. White, 
                                
                                    John B. Carlin
                                
                                et al.
                            
                            (2009). 
                            Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. 
                            BMJ
                            , 338
                            (jun29 1)
                            , b2393-b2393.
                            https://doi.org/10.1136/bmj.b2393
                        

Identifiers

DOI: 10.1136/bmj.b2393