A Bayesian approach to correct for unmeasured or semi-unmeasured confounding in survival data using multiple validation data sets
Purpose: The existence of unmeasured confounding can clearly undermine the validity of an observational study. Methods of conducting sensitivity analyses to evaluate the impact of unmeasured confounding are well established. However, application of such methods to survival data (“time-to-event” o...
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| Hoofdauteurs: | , , , , , |
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| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
Milano University Press
2022-03-01
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| Reeks: | Epidemiology, Biostatistics and Public Health |
| Online toegang: | https://riviste.unimi.it/index.php/ebph/article/view/17474 |
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