Dimension reduction for right-censored survival regression: transformation approach

- Journal title : Communications for Statistical Applications and Methods
- Volume 23, Issue 3, 2016, pp.259-268
- Publisher : The Korean Statistical Society
- DOI : 10.5351/CSAM.2016.23.3.259

Title & Authors

Dimension reduction for right-censored survival regression: transformation approach

Yoo, Jae Keun; Kim, Sung-Jin; Seo, Bi-Seul; Shin, Hyejung; Sim, Su-Ah;

Yoo, Jae Keun; Kim, Sung-Jin; Seo, Bi-Seul; Shin, Hyejung; Sim, Su-Ah;

Abstract

High-dimensional survival data with large numbers of predictors has become more common. The analysis of such data can be facilitated if the dimensions of predictors are adequately reduced. Recent studies show that a method called sliced inverse regression (SIR) is an effective dimension reduction tool in high-dimensional survival regression. However, it faces incapability in implementation due to a double categorization procedure. This problem can be overcome in the right-censoring type by transforming the observed survival time and censoring status into a single variable. This provides more flexibility in the categorization, so the applicability of SIR can be enhanced. Numerical studies show that the proposed transforming approach is equally good to (or even better) than the usual SIR application in both balanced and highly-unbalanced censoring status. The real data example also confirms its practical usefulness, so the proposed approach should be an effective and valuable addition to usual statistical practitioners.

Keywords

bivariate slicing;right-censored data;sliced inverse regression;sufficient dimension reduction;survival regression;transformation method;unbalanced censoring status;

Language

English

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