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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...
Tallennettuna:
| Julkaisussa: | BMC Med Res Methodol |
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| Päätekijät: | , , , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
BioMed Central
2018
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6029422/ https://ncbi.nlm.nih.gov/pubmed/29969993 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12874-018-0527-5 |
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