ROBUST JOINT MODELLING OF LEFT-CENSORED LONGITUDINAL DATA AND SURVIVAL DATA WITH APPLICATION TO HIV VACCINE STUDIES

被引：0
|
作者：
Yu, Tingting [1 ,2 ]
Wu, Lang [3 ]
Qiu, Jin [4 ]
Gilbert, Peter B. [5 ]
机构：
[1] Harvard Pilgrim Hlth Care Inst, Boston, MA 02215 USA
[2] Harvard Med Sch, Boston, MA 02115 USA
[3] Univ British Columbia, Dept Stat, Vancouver, BC, Canada
[4] Zhejiang Univ Finance & Econ, Dept Stat, Hangzhou, Peoples R China
[5] Univ Washington, Dept Biostat, Seattle, WA USA
来源：
ANNALS OF APPLIED STATISTICS | 2023年 / 17卷 / 02期
关键词：
Biomarker; outliers; robust joint model; h-likelihood; left censoring; INFERENCE;
D O I：
10.1214/22-AOAS1656
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：
In jointly modelling longitudinal and survival data, the longitudinal data may be complex in the sense that they may contain outliers and may be left censored. Motivated from an HIV vaccine study, we propose a robust method for joint models of longitudinal and survival data, where the outliers in longi-tudinal data are addressed using a multivariate t-distribution for b-outliers and using an M-estimator for e-outliers. We also propose a computationally effi-cient method for approximate likelihood inference. The proposed method is evaluated by simulation studies. Based on the proposed models and method, we analyze the HIV vaccine data and find a strong association between lon-gitudinal biomarkers and the risk of HIV infection.
引用
收藏
页码：1017 / 1037
页数：21
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