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Accounting for Uncertainty in Confounder and Effect Modifier Selection when Estimating Average Causal Effects in Generalized Linear Models
Confounder selection and adjustment are essential elements of assessing the causal effect of an exposure or treatment in observational studies. Building upon work by Wang et al. (2012) and Lefebvre et al. (2014), we propose and evaluate a Bayesian method to estimate average causal effects in studies...
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| Gepubliceerd in: | Biometrics |
|---|---|
| Hoofdauteurs: | , , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
2015
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4575246/ https://ncbi.nlm.nih.gov/pubmed/25899155 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1111/biom.12315 |
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