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Non-proportional hazards data are routinely encountered in randomizedclinical trials. In such cases, classic Cox proportional hazards model cansuffer from severe power loss, with difficulty in interpretation of theestimated hazard ratio since the treatment effect varies over time. We proposeCauchyCP, an omnibus test of change-point Cox regression models, to overcomeboth challenges while detecting signals of non-proportional hazards patterns.Extensive simulation studies demonstrate that, compared to existing treatmentcomparison 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 morecomputationally efficient. The superior performance of CauchyCP is furtherillustrated using retrospective analyses of two randomized clinical trialdatasets and a pharmacogenetic biomarker study dataset. The R package$\textit{CauchyCP}$ is publicly available on CRAN.

CauchyCP: a powerful test under non-proportional hazards using Cauchy combination of change-point Cox regressions