In large-scale genomic analyses that focus on genotype-phenotype associations, such as genome wide association studies, it is desirable to have numerically and statistically robust procedures to test the stochastic independence null hypothesis against certain alternatives. In this study, I developed a novel test procedure called the correlation profile test (CPT) for testing genomic associations with phenotypes of failure times subject to the right censoring and competing risks. Compared with popular choices of semiparametric and nonparametric methods, CPT has three advantages: it is numerically more robust because it solely relies on sample moments; it is more robust against the violation of the proportional hazards condition; and it is more flexible in handling various failure and censoring scenarios.
Cheng, C. Exploratory failure time analysis in large scale genomics. Computational Statistics and Data Analysis, 95:192-206, 2016. NIHMS733642. [doi:http://dx.doi.org/10.1016/j.csda.2015.10.004]