Estimating causal effects of time-dependent exposures on a binary endpoint in a high-dimensional setting

BACKGROUND: Recently, the intervention calculus when the DAG is absent (IDA) method was developed to estimate lower bounds of causal effects from observational high-dimensional data. Originally it was introduced to assess the effect of baseline biomarkers which do not vary over time. However, in man...

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Detalles Bibliográficos
Autores principales: Asvatourian, Vahé, Coutzac, Clélia, Chaput, Nathalie, Robert, Caroline, Michiels, Stefan, Lanoy, Emilie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029422/
https://www.ncbi.nlm.nih.gov/pubmed/29969993
http://dx.doi.org/10.1186/s12874-018-0527-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029422/
https://www.ncbi.nlm.nih.gov/pubmed/29969993
http://dx.doi.org/10.1186/s12874-018-0527-5

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