Improvement Screening for Ultra-High Dimensional Data with Censored Survival Outcomes and Varying Coefficients

Yue Mu and Li Jialiang ()
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Yue Mu: Jialiang Li,Department of Statistics and Applied Probability,National University of Singapore, Singapore, Singapore
Li Jialiang: Yue Mu, National University of Singapore, SingaporeSingapore

The International Journal of Biostatistics, 2017, vol. 13, issue 1, 16

Abstract: Motivated by risk prediction studies with ultra-high dimensional bio markers, 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.

Keywords: diagnostic accuracy improvement; integrated discrimination improvement; net reclassification improvement; C-statistics; varying-coefficient model; ultra-high dimensional data screening (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/ijb-2017-0024

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