Affiliations: Department of Preventive Medicine, Keck School of
Medicine, University of Southern California, Los Angeles, California 90089,
USA
Note: [] Corresponding author: Kimberly D. Siegmund, Department of
Preventive Medicine, USC Keck School of Medicine, 1540 Alcazar Street, CHP-220,
Los Angeles, CA 90089, USA. Tel.: +1 323 442 1310; Fax: +1 323 442 2349;
E-mail: kims@usc.edu
Abstract: We summarize standard and novel statistical methods for evaluating
the classification accuracy of DNA methylation markers. The choice of method
will depend on the type of marker studied (qualitative/quantitative), the
number of markers, and the type of outcome (time-invariant/time-varying). A
minimum of two error rates are needed for assessing marker accuracy: the
true-positive fraction and the false-positive fraction. Measures of association
that are computed from the combination of these error rates, such as the odds
ratio or relative risk, are not informative about classification accuracy. We
provide an example of a DNA methylation marker that is strongly associated with
time to death (logrank p=0.0003) that is not a good
classifier as evaluated by the true-positive and false-positive fractions.
Finally, we would like to emphasize the importance of study design. Markers can
behave differently in different groups of individuals. It is important to know
what factors may affect the accuracy of a marker and in which subpopulations
the marker may be more accurate. Such an understanding is extremely important
when comparing marker accuracy in two groups of subjects.
Keywords: Sensitivity, specificity, ROC curves, survival data
Journal: Disease Markers, vol. 23, no. 1-2, pp. 113-120, 2007
