  Identifying associations among biological variables is a major challenge in
modern quantitative biological research, particularly given the systemic and
statistical noise endemic to biological systems. Drug sensitivity data has
proven to be a particularly challenging field for identifying associations to
inform patient treatment. To address this, we introduce two semi-parametric
variations on the commonly used Concordance Index: the robust Concordance Index
and the kernelized Concordance Index (rCI, kCI), which incorporate measurements
about the noise distribution from the data. We demonstrate that common
statistical tests applied to the concordance index and its variations fail to
control for false positives, and introduce efficient implementations to compute
p-values using adaptive permutation testing. We then evaluate the statistical
power of these coefficients under simulation and compare with Pearson and
Spearman correlation coefficients. Finally, we evaluate the various statistics
in matching drugs across pharmacogenomic datasets. We observe that the rCI and
kCI are better powered than the concordance index in simulation and show some
improvement on real data. Surprisingly, we observe that the Pearson correlation
was the most robust to measurement noise among the different metrics.
