  Non-proportional hazards data are routinely encountered in randomized
clinical trials. In such cases, classic Cox proportional hazards model can
suffer from severe power loss, with difficulty in interpretation of the
estimated hazard ratio since the treatment effect varies over time. We propose
CauchyCP, an omnibus test of change-point Cox regression models, to overcome
both challenges while detecting signals of non-proportional hazards patterns.
Extensive simulation studies demonstrate that, compared to existing treatment
comparison tests under non-proportional hazards, the proposed CauchyCP test 1)
controls the type I error better at small $\alpha$ levels ($< 0.01$); 2)
increases the power of detecting time-varying effects; and 3) is more
computationally efficient. The superior performance of CauchyCP is further
illustrated using retrospective analyses of two randomized clinical trial
datasets and a pharmacogenetic biomarker study dataset. The R package
$\textit{CauchyCP}$ is publicly available on CRAN.
