How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies

BACKGROUND: Analysis of competing risks is commonly achieved through a cause specific or a subdistribution framework using Cox or Fine & Gray models, respectively. The estimation of treatment effects in observational data is prone to unmeasured confounding which causes bias. There has been limit...

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Detalles Bibliográficos
Autores principales: Barrowman, Michael Andrew, Peek, Niels, Lambie, Mark, Martin, Glen Philip, Sperrin, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668192/
https://www.ncbi.nlm.nih.gov/pubmed/31366331
http://dx.doi.org/10.1186/s12874-019-0808-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668192/
https://www.ncbi.nlm.nih.gov/pubmed/31366331
http://dx.doi.org/10.1186/s12874-019-0808-7

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