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...

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Detalles Bibliográficos
Autores principales: Sperrin, Matthew, Martin, Glen P., Pate, Alexander, Van Staa, Tjeerd, Peek, Niels, Buchan, Iain
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Research Articles
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

Internet

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

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