Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/137728
Title: IMPROVEMENT SCREENING FOR ULTRA-HIGH DIMENSIONAL DATA WITH CENSORED SURVIVAL OUTCOMES AND VARYING COEFFICIENTS
Authors: YUE MU
ORCID iD:   orcid.org/0000-0001-5012-0690
Keywords: Integrated discrimination improvement; Net reclassification improvement; C-statistics; Varying-coefficient model; Ultra-high dimensional data
Issue Date: 7-Aug-2017
Source: YUE MU (2017-08-07). IMPROVEMENT SCREENING FOR ULTRA-HIGH DIMENSIONAL DATA WITH CENSORED SURVIVAL OUTCOMES AND VARYING COEFFICIENTS. ScholarBank@NUS Repository.
Abstract: Motivated by risk prediction studies with ultra-high dimensional biomarkers, we propose a novel improvement screening methodology. Accurate risk prediction can be quite useful for patient treatment selection, prevention strategy or disease management in evidence-based medicine. The question of how to choose new markers in addition to the conventional ones is especially important. In the past decade, a number of new measures for quantifying the added value from the new markers were proposed, among which the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) stand out. Meanwhile, C-statistics are routinely used to quantify the capacity of the estimated risk score in discriminating among subjects with different event times. In this paper, we will examine these improvement statistics as well as the norm-based approach for evaluating the incremental values of new markers and compare these four measures by analyzing ultra-high dimensional censored survival data. In particular, we consider Cox proportional hazards models with varying coefficients. All measures perform very well in simulations and we illustrate our methods in an application to a lung cancer study.
URI: http://scholarbank.nus.edu.sg/handle/10635/137728
Appears in Collections:Ph.D Theses (Open)

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