How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies
Abstract 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 l...
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| Autori principali: | , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
BMC
2019-07-01
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| Serie: | BMC Medical Research Methodology |
| Soggetti: | |
| Accesso online: | http://link.springer.com/article/10.1186/s12874-019-0808-7 |
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