Open AccessAccounting for missing data in statistical analyses: multiple imputation is not always the answer
Author(s) -
Rachael A. Hughes,
Jon Heron,
Jonathan A C Sterne,
Kate Tilling
Publication year - 2019
Publication title -
international journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.406
H-Index - 208
eISSN - 1464-3685
pISSN - 0300-5771
DOI - 10.1093/ije/dyz032
Subject(s) - missing data , imputation (statistics) , covariate , computer science , data mining , statistics , selection bias , econometrics , mathematics
Missing data are unavoidable in epidemiological research, potentially leading to bias and loss of precision. Multiple imputation (MI) is widely advocated as an improvement over complete case analysis (CCA). However, contrary to widespread belief, CCA is preferable to MI in some situations.
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