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Using latent variable modeling and multiple imputation to calibrate rater bias in diagnosis assessment
We present an approach that uses latent variable modeling and multiple imputation to correct rater bias when one group of raters tends to be more lenient in assigning a diagnosis than another. Our method assumes there exists an unobserved moderate category of patient that is assigned a positive diag...
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| Asıl Yazarlar: | , , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
2010
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| Konular: | |
| Online Erişim: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3058328/ https://ncbi.nlm.nih.gov/pubmed/21204122 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/sim.4109 |
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