A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study

BACKGROUND: Missing data is a common problem in epidemiological studies, and is particularly prominent in longitudinal data, which involve multiple waves of data collection. Traditional multiple imputation (MI) methods (fully conditional specification (FCS) and multivariate normal imputation (MVNI))...

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Detalles Bibliográficos
Autores principales: De Silva, Anurika Priyanjali, Moreno-Betancur, Margarita, De Livera, Alysha Madhu, Lee, Katherine Jane, Simpson, Julie Anne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5526258/
https://www.ncbi.nlm.nih.gov/pubmed/28743256
http://dx.doi.org/10.1186/s12874-017-0372-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5526258/
https://www.ncbi.nlm.nih.gov/pubmed/28743256
http://dx.doi.org/10.1186/s12874-017-0372-y

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