Missing link survival analysis with applications to available pandemic data

It is shown how to overcome a new missing data problem in survival analysis. Iterative nonparametric techniques are utilized and the missing data information is both estimated and used for further estimation in each iterative step. Theory is developed and a good finite sample performance is illustra...

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Detalles Bibliográficos
Autores principales: Gámiz, María Luz, Mammen, Enno, Martínez-Miranda, María Dolores, Nielsen, Jens Perch
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8666881/
https://www.ncbi.nlm.nih.gov/pubmed/34924652
http://dx.doi.org/10.1016/j.csda.2021.107405
Descripción
Sumario:It is shown how to overcome a new missing data problem in survival analysis. Iterative nonparametric techniques are utilized and the missing data information is both estimated and used for further estimation in each iterative step. Theory is developed and a good finite sample performance is illustrated by simulations. The main motivation is an application to French data on the temporal development of the number of hospitalized Covid-19 patients.

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