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Two-Sample Tests of Area-Under-the-Curve in the Presence of Missing Data

Author

Listed:
  • Spritzler John

    (Harvard University)

  • DeGruttola Victor G

    (Harvard University)

  • Pei Lixia

    (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.

Suggested Citation

  • Spritzler John & DeGruttola Victor G & Pei Lixia, 2008. "Two-Sample Tests of Area-Under-the-Curve in the Presence of Missing Data," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-18, January.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:1
    DOI: 10.2202/1557-4679.1068
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    Cited by:

    1. Brent A. Johnson & Heather Ribaudo & Roy M. Gulick & Joseph J. Eron Jr., 2013. "Modeling Clinical Endpoints as a Function of Time of Switch to Second-Line ART with Incomplete Data on Switching Times," Biometrics, The International Biometric Society, vol. 69(3), pages 732-740, September.

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