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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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Détails bibliographiques
Auteurs principaux: Spritzler, John, DeGruttola, Victor G, Pei, Lixia
Format: Artigo
Langue:Inglês
Publié: Berkeley Electronic Press 2008
Sujets:
Accès en ligne: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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