Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis
By Rolf H. H. Groenwold, Ian R. White, A. Donders, James R. Carpenter, Douglas G. Altman, Karel G. M. Moons. Canadian Medical Association Journal, 2012
459 citations recorded by the source corpus.
Missing data are a frequently encountered problem in epidemiologic and clinical research.[1][1],[2][2] One approach is to include in the analysis only those participants without missing observations (complete or available case analysis).[1][1]–[4][3] However, in addition to reducing statistical
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/afb609ae7118e08d68d74195e99d904c7ad318c2
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Citations in the corpus
- Post-covid syndrome in individuals admitted to hospital with covid-19: retrospective cohort study (2021, methodology): “33 But the missing mechanism in clinical records might to some extent be “missing not at random” (eg, individuals who are neither underweight nor overweight could be less likely to have their body mass index measured), which would preclude the use of standard imputation techniques.”