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Covariate Selection in High-Dimensional Propensity Score Analyses of Treatment Effects in Small Samples
To reduce bias by residual confounding in nonrandomized database studies, the high-dimensional propensity score (hd-PS) algorithm selects and adjusts for previously unmeasured confounders. The authors evaluated whether hd-PS maintains its capabilities in small cohorts that have few exposed patients...
Tallennettuna:
| Päätekijät: | , , , |
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| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
Oxford University Press
2011
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3145392/ https://ncbi.nlm.nih.gov/pubmed/21602301 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/aje/kwr001 |
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