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Two-Sample Tests of Area-Under-the-Curve in the Presence of Missing Data
The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MC...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Berkeley Electronic Press
2008
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2835452/ https://ncbi.nlm.nih.gov/pubmed/20231907 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.2202/1557-4679.1068 |
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