Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example Using the Random Hot Deck Approach

PURPOSE: Researchers often use model-based multiple imputation to handle missing at random data to minimize bias. However, constraints within the data may sometimes result in implausible values, making model-based imputation infeasible. In these contexts, we illustrate how random hot deck imputation...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Chinchin, Stokes, Tyrel, Steele, Russell J, Wedderkopp, Niels, Shrier, Ian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675352/
https://www.ncbi.nlm.nih.gov/pubmed/36411940
http://dx.doi.org/10.2147/CLEP.S368303

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675352/
https://www.ncbi.nlm.nih.gov/pubmed/36411940
http://dx.doi.org/10.2147/CLEP.S368303

Ejemplares similares