Using directed acyclic graphs to determine whether multiple imputation or subsample-multiple imputation estimates of an exposure-outcome association are unbiased.
Missing data are a pervasive problem in epidemiology, with multiple imputation (MI) a commonly used analysis method. MI is valid when data are missing at random (MAR). However, definitions of MAR with multiple incomplete variables are not easily interpretable and descriptions of graphical model-base...
| Publicado en: | American Journal of Epidemiology Vol. 195; no. 2; pp. 505 - 515 |
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| Autores principales: | , , , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |