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Article
Two-Sample Tests of Area-Under-the-Curve in the Presence of Missing Data
The International Journal of Biostatistics (2010)
  • John Spritzler, Harvard University
  • Victor G DeGruttola, Harvard University
  • Lixia Pei, Harvard University
Abstract
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 MCAR and another that has good properties when the data are missing at random (MAR), provided that the pattern of missingness is monotonic. In addition, we discuss other non-parametric tests of hypotheses that are similar, but not identical, to the hypothesis of equal AUCs, but that often have better statistical properties than do AUC tests and may be more scientifically appropriate for many settings.
Keywords
  • AUC,
  • bias,
  • missing data,
  • test
Publication Date
March 30, 2010
Citation Information
John Spritzler, Victor G DeGruttola and Lixia Pei. "Two-Sample Tests of Area-Under-the-Curve in the Presence of Missing Data" The International Journal of Biostatistics Vol. 4 Iss. 1 (2010)
Available at: http://works.bepress.com/john_spritzler/1/