title: Analysis of longitudinal data in the presence of informative observational times and a dependent terminal event, with application to medical cost data.
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.
authors.affiliation: Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia 22908-0717, U.S.A.
authors.forename: Lei
authors.initials: L
authors.lastname: Liu
authors.affiliation: Department of Biostatistics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas 77030, U.S.A.
authors.forename: Xuelin
authors.initials: X
authors.lastname: Huang
authors.affiliation: Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia 22908-0717, U.S.A.
authors.forename: John
authors.initials: J
authors.lastname: O'Quigley
mesh_terms: D000368:Aged; D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006333:Heart Failure / Q000191:economics / Q000401:mortality / Q000628:therapy; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D018803:Models, Economic; D015233:Models, Statistical; D016016:Proportional Hazards Models; D016019:Survival Analysis