Conditional screening for ultra-high dimensional covariates with survival outcomes

Hyokyoung G. Hong (), Jian Kang () and Yi Li ()
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Hyokyoung G. Hong: Michigan State University
Jian Kang: University of Michigan
Yi Li: University of Michigan

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 1, No 4, 45-71

Abstract: Abstract 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 priorily 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 dataset. We are pleased to dedicate this work to Jack Kalbfleisch, who has made instrumental contributions to the development of modern methods of analyzing survival data.

Keywords: Conditional screening; Cox model; Diffuse large B-cell lymphoma; High-dimensional variable screening (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10985-016-9387-7

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