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Using marginal structural models to adjust for treatment drop‐in when developing clinical prediction models
Clinical prediction models (CPMs) can inform decision making about treatment initiation, which requires predicted risks assuming no treatment is given. However, this is challenging since CPMs are usually derived using data sets where patients received treatment, often initiated postbaseline as “trea...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
John Wiley and Sons Inc.
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6282523/ https://www.ncbi.nlm.nih.gov/pubmed/30073700 http://dx.doi.org/10.1002/sim.7913 |