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Exploring causality mechanism in the joint analysis of longitudinal and survival data
Author(s) -
Liu Lei,
Zheng Cheng,
Kang Joseph
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7838
Subject(s) - causality (physics) , biomarker , disease , medicine , clinical trial , survival analysis , mechanism (biology) , longitudinal data , cirrhosis , random effects model , intensive care medicine , oncology , computer science , data mining , meta analysis , biology , biochemistry , philosophy , physics , epistemology , quantum mechanics
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.