A0352
Title: Conditional inference for ultrahigh-dimensional additive hazards model
Authors: Meiling Hao - University of International Business and Economics (China) [presenting]
Abstract: In the realm of high-throughput genomic data, modeling with ultra-high-dimensional covariates and censored survival outcomes is of great importance. The conditional inference is conducted for the ultra-high-dimensional additive hazards model, allowing both the covariates of interest and nuisance covariates to be ultrahigh-dimensional. The presence of right censorship with survival outcomes adds an extra layer of complexity to the original data structure, posing significant challenges for the ultrahigh-dimensional additive hazards model. To address this, an innovative test statistic is introduced based on the quadratic norm of the score function. Moreover, when there is a high correlation between the covariates of interest and nuisance covariates, a decorrelated score function-based test statistic is proposed to enhance statistical power. Additionally, the limiting distribution of the test statistics is established under both the null and local alternative hypotheses, further enhancing the computational appeal of the approach. The proposed statistics are thoroughly evaluated through extensive simulation studies and applied to two real data examples.
