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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Japan Epidemiological Association
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429147/ https://www.ncbi.nlm.nih.gov/pubmed/32684529 http://dx.doi.org/10.2188/jea.JE20200226 |