Abstract：
:In longitudinal observational studies, repeated measures are often taken at informative observation times. Also, there may exist a dependent terminal event such as death that stops the follow-up. For example, patients in poorer health are more likely to seek medical treatment and their medical cost for each visit tends to be higher. They are also subject to a higher mortality rate. In this article, we propose a random effects model of repeated measures in the presence of both informative observation times and a dependent terminal event. Three submodels are used, respectively, for (1) the intensity of recurrent observation times, (2) the amount of repeated measure at each observation time, and (3) the hazard of death. Correlated random effects are incorporated to join the three submodels. The estimation can be conveniently accomplished by Gaussian quadrature techniques, e.g., SAS Proc NLMIXED. An analysis of the cost-accrual process of chronic heart failure patients from the clinical data repository at the University of Virginia Health System is presented to illustrate the proposed method.
journal_name
Biometricsjournal_title
Biometricsauthors
Liu L,Huang X,O'Quigley Jdoi
10.1111/j.1541-0420.2007.00954.xsubject
Has Abstractpub_date
2008-09-01 00:00:00pages
950-8issue
3eissn
0006-341Xissn
1541-0420pii
BIOM954journal_volume
64pub_type
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