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Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips
Epidemiologists are increasingly encountering complex longitudinal data, in which exposures and their confounders vary during follow-up. When a prior exposure affects the confounders of the subsequent exposures, estimating the effects of the time-varying exposures requires special statistical techni...
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| Foilsithe in: | J Epidemiol |
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| Main Authors: | , |
| Formáid: | Artigo |
| Teanga: | Inglês |
| Foilsithe: |
Japan Epidemiological Association
2020
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| Ábhair: | |
| Rochtain Ar Líne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7429147/ https://ncbi.nlm.nih.gov/pubmed/32684529 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.2188/jea.JE20200226 |
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