Cargando…
Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with incomplete data
BACKGROUND: Multiple Imputation as usually implemented assumes that data are Missing At Random (MAR), meaning that the underlying missing data mechanism, given the observed data, is independent of the unobserved data. To explore the sensitivity of the inferences to departures from the MAR assumption...
Autores principales: | Héraud-Bousquet, Vanina, Larsen, Christine, Carpenter, James, Desenclos, Jean-Claude, Le Strat, Yann |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3537570/ https://www.ncbi.nlm.nih.gov/pubmed/22681630 http://dx.doi.org/10.1186/1471-2288-12-73 |
Ejemplares similares
-
Multiple imputation for an incomplete covariate that is a ratio
por: Morris, Tim P, et al.
Publicado: (2014) -
Multiple imputation and analysis for high‐dimensional incomplete proteomics data
por: Yin, Xiaoyan, et al.
Publicado: (2015) -
A three-source capture-recapture estimate of the number of new HIV diagnoses in children in France from 2003–2006 with multiple imputation of a variable of heterogeneous catchability
por: Héraud-Bousquet, Vanina, et al.
Publicado: (2012) -
Multiple imputation for analysis of incomplete data in distributed health data networks
por: Chang, Changgee, et al.
Publicado: (2020) -
Considerations for Using Multiple Imputation in Propensity Score-Weighted Analysis – A Tutorial with Applied Example
por: Eiset, Andreas Halgreen, et al.
Publicado: (2022)