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 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.
Spritzler
John
J
Harvard University. spritz@sdac.harvard.edu
DeGruttola
Victor G
VG
Pei
Lixia
L
D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy / Q000235:genetics / Q000821:virology; D019380:Anti-HIV Agents / Q000627:therapeutic use; D019540:Area Under Curve; D015982:Bias; D056808:Biostatistics; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D024882:Drug Resistance, Viral / Q000235:genetics; D006801:Humans; D015233:Models, Statistical; D012367:RNA, Viral / Q000097:blood; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D018709:Statistics, Nonparametric
D019380:Anti-HIV Agents; D012367:RNA, Viral