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Conditional Screening for Ultra-high Dimensional Covariates with Survival Outcomes
Identifying important biomarkers that are predictive for cancer patients'
prognosis is key in gaining better insights into the biological influences on
the disease and has become a critical component of precision medicine. The
emergence of large-scale biomedical survival studies, which typically involve
excessive number of biomarkers, has brought high demand in designing efficient
screening tools for selecting predictive biomarkers. The vast amount of
biomarkers defies any existing variable selection methods via regularization.
The recently developed variable screening methods, though powerful in many
practical setting, fail to incorporate prior information on the importance of
each biomarker and are less powerful in detecting marginally weak while jointly
important signals. We propose a new conditional screening method for survival
outcome data by computing the marginal contribution of each biomarker given
priorly known biological information. This is based on the premise that some
biomarkers are known to be associated with disease outcomes a priori. Our
method possesses sure screening properties and a vanishing false selection
rate. The utility of the proposal is further confirmed with extensive
simulation studies and analysis of a Diffuse large B-cell lymphoma (DLBCL)
dataset.Comment: 34 pages, 3 figure
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