Exploring causality mechanism in the joint analysis of longitudinal and survival data

被引：12
|
作者：
Liu, Lei [1 ]
Zheng, Cheng [2 ]
Kang, Joseph [3 ]
机构：
[1] Washington Univ, Div Biostat, St Louis, MO 63110 USA
[2] Univ Wisconsin, Joseph J Zilber Sch Publ Hlth, Milwaukee, WI 53201 USA
[3] Ctr Dis Control & Prevent, Atlanta, GA USA
来源：
STATISTICS IN MEDICINE | 2018年 / 37卷 / 26期
基金：
美国医疗保健研究与质量局;
关键词：
interaction; mediation analysis; moderator; repeated measures; shared random effects; SURROGATE END-POINT; MEDIATION ANALYSIS; CLINICAL-TRIALS; RECURRENT EVENTS; PROSTATE-CANCER; MODELS; MORTALITY;
D O I：
10.1002/sim.7838
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In many biomedical studies, disease progress is monitored by a biomarker over time, eg, repeated measures of CD4 in AIDS and hemoglobin in end-stage renal disease patients. The endpoint of interest, eg, death or diagnosis of a specific disease, is correlated with the longitudinal biomarker. In this paper, we examine and compare different models of longitudinal and survival data to investigate causal mechanisms, specifically, those related to the role of random effects. We illustrate the methods by data from two clinical trials: an AIDS study and a liver cirrhosis study.
引用
收藏
页码：3733 / 3744
页数：12
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