A unified approach to variable selection for Cox's proportional hazards model with interval-censored failure time data

被引:9
作者
Du, Mingyue [1 ]
Zhao, Hui [2 ]
Sun, Jianguo [3 ]
机构
[1] Hong Kong Polytech Univ, Dept Appl Math, Hung Hom, Hong Kong, Peoples R China
[2] Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430073, Hubei, Peoples R China
[3] Univ Missouri, Dept Stat, Columbia, MO 65211 USA
关键词
Bernstein polynomials; case K interval-censored data; informative censoring; penalized procedure; variable selection; NONCONCAVE PENALIZED LIKELIHOOD; REGRESSION-ANALYSIS; ADAPTIVE LASSO;
D O I
10.1177/09622802211009259
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Cox's proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer's Disease Neuroimaging Initiative study that motivated this study.
引用
收藏
页码:1833 / 1849
页数:17
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