B0791
Title: Statistical inference for Cox proportional hazards models with a diverging number of covariates
Authors: Yi Li - University of Michigan (United States) [presenting]
Abstract: For statistical inference on regression models with a diverging number of covariates, the existing literature typically makes sparsity assumptions on the inverse of the Fisher information matrix. Such assumptions, however, are often violated under Cox proportion hazards models, leading to biased estimates with under-coverage confidence intervals. A modified debiased lasso method is proposed, which solves a series of quadratic programming problems to approximate the inverse information matrix without posing sparse matrix assumptions. Asymptotic results are established for the estimated regression coefficients when the dimension of covariates diverges with the sample size. As demonstrated by simulations, the proposed method provides consistent estimates and confidence intervals with nominal coverage probabilities. The utility of the method is further demonstrated by assessing the effects of genetic markers on patients' overall survival with the Boston Lung Cancer Survival Cohort, a large-scale epidemiology study investigating mechanisms underlying lung cancer.
