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Abstract:
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Technology advances facilitate collection of high-dimensional covariate information including microarray, proteomic and SNP data. Challenges have frequently been encountered when scientists attempt to understand the association, if any, between the high-dimensional covariates of the subjects and the survival time, which is complicated due to censoring. In this work, we develop a new rank-based approach that enables us to tackle the variable selection problem for various semiparametric models for life-time data via the novel pseudo value method. While there has only been sure independence screening (SIS) for ultra-high dimensional data modelled by Cox proportional hazards models in literature, our methodology can handle a much broader class of semiparametric models including general transformation models and the accelerated failure time model. Numerical studies have demonstrated promising performance that is comparable to the (iterative) sure independence screening (SIS). Our method was also applied to analyse Diffuse large-B-cell lymphoma data, which discovered potential genes that can be influential.
This is a joint work with Ming Gao Gu and Yongze Xu
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