Exploratory failure time analysis in large scale genomics

Cheng Cheng

Computational Statistics & Data Analysis, 2016, vol. 95, issue C, 192-206

Abstract: In large scale genomic analyses dealing with detecting genotype–phenotype associations, such as genome wide association studies (GWAS), it is desirable to have numerically and statistically robust procedures to test the stochastic independence null hypothesis against certain alternatives. Motivated by a special case in a GWAS, a novel test procedure called Correlation Profile Test (CPT) is developed for testing genomic associations with failure-time phenotypes subject to right censoring and competing risks. Performance and operating characteristics of CPT are investigated and compared to existing approaches, by a simulation study and on a real dataset. Compared to 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. CPT is a general approach to testing the null hypothesis of stochastic independence between a failure event point process and any random variable; thus it is widely applicable beyond genomic studies.

Keywords: Censored failure time data; Exploratory analysis; Failure event point process; Stochastically monotone dependence; Correlation Profile Test; Hybrid permutation test; Large scale genomic analysis; GWAS; Genotype–phenotype association (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:95:y:2016:i:c:p:192-206

DOI: 10.1016/j.csda.2015.10.004

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