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A general instrumental variable framework for regression analysis with outcome missing not at random
The instrumental variable (IV) design is a well-known approach for unbiased evaluation of causal effects in the presence of unobserved confounding. In this paper, we study the IV approach to account for selection bias in regression analysis with outcome missing not at random. In such a setting, a va...
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| Pubblicato in: | Biometrics |
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| Autori principali: | , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2017
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5569006/ https://ncbi.nlm.nih.gov/pubmed/28230909 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1111/biom.12670 |
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