TY - JOUR
T1 - Analysis of longitudinal data in the presence of informative observational times and a dependent terminal event, with application to medical cost data
AU - Liu, Lei
AU - Huang, Xuelin
AU - O'Quigley, John
PY - 2008/9
Y1 - 2008/9
N2 - 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.
AB - 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.
KW - Frailty model
KW - Informative drop-out
KW - Longitudinal medical costs
KW - Piecewise constant baseline hazard
KW - Proportional hazards model
KW - Recurrent marker
KW - Survival analysis
UR - http://www.scopus.com/inward/record.url?scp=49749152044&partnerID=8YFLogxK
U2 - 10.1111/j.1541-0420.2007.00954.x
DO - 10.1111/j.1541-0420.2007.00954.x
M3 - Article
C2 - 18162110
AN - SCOPUS:49749152044
SN - 0006-341X
VL - 64
SP - 950
EP - 958
JO - Biometrics
JF - Biometrics
IS - 3
ER -