BIOMARKER CHANGE-POINT ESTIMATION WITH RIGHT CENSORING IN LONGITUDINAL STUDIES

被引:3
|
作者
Tang, Xiaoying [1 ]
Miller, Michael I. [2 ]
Younes, Laurent [2 ]
机构
[1] Sun Yat Sen Univ, SYSU CMU Joint Inst Engn, Guangzhou Higher Educ Mega Ctr, 132 East Waihuan Rd, Guangzhou 510006, Guangdong, Peoples R China
[2] Johns Hopkins Univ, Ctr Imaging Sci, 3400 N Charles St, Baltimore, MD 21218 USA
来源
ANNALS OF APPLIED STATISTICS | 2017年 / 11卷 / 03期
基金
美国国家卫生研究院; 美国国家科学基金会; 中国国家自然科学基金;
关键词
Change-point estimation; right censoring; medical imaging; MILD COGNITIVE IMPAIRMENT; HAZARD REGRESSION-MODEL; ALZHEIMERS-DISEASE; 2-PHASE REGRESSION; PREDICT DEMENTIA; AMYGDALA; ATROPHY; SHAPE; DIFFEOMORPHOMETRY; INFERENCE;
D O I
10.1214/17-AOAS1056
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We consider in this paper a statistical two-phase regression model in which the change point of a disease biomarker is measured relative to another point in time, such as the manifestation of the disease, which is subject to right-censoring (i.e., possibly unobserved over the entire course of the study). We develop point estimation methods for this model, based on maximum likelihood, and bootstrap validation methods. The effectiveness of our approach is illustrated by numerical simulations, and by the estimation of a change point for amygdalar atrophy in the context of Alzheimer's disease, wherein it is related to the cognitive manifestation of the disease.
引用
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页码:1738 / 1762
页数:25
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