title: A joint frailty model for survival and gap times between recurrent events.
abstract: Therapy for patients with a recurrent disease focuses on delaying disease recurrence and prolonging survival. A common analysis approach for such data is to estimate the distribution of disease-free survival, that is, the time to the first disease recurrence or death, whichever happens first. However, treating death similarly as disease recurrence may give misleading results. Also considering only the first recurrence and ignoring subsequent ones can result in loss of statistical power. We use a joint frailty model to simultaneously analyze disease recurrences and survival. Separate parameters for disease recurrence and survival are used in the joint model to distinguish treatment effects on these two types of events. The correlation between disease recurrences and survival is taken into account by a shared frailty. The effect of disease recurrence on survival can also be estimated by this model. The EM algorithm is used to fit the model, with Markov chain Monte Carlo simulations in the E-steps. The method is evaluated by simulation studies and illustrated through a study of patients with heart failure. Sensitivity analysis for the parametric assumption of the frailty distribution is assessed by simulations.
authors: Anderson Cancer Center, Department of Biostatistics, The University of Texas, M. D. 1515 Holcombe Boulevard, Unit 447, Houston, Texas 77030, USA. xlhuang@mdanderson.org
authors: Xuelin
authors: X
authors: Huang
authors: Lei
authors: L
authors: Liu
mesh_terms: D000368:Aged; D000465:Algorithms; D001699:Biometry; D018572:Disease-Free Survival; D005260:Female; D006333:Heart Failure / Q000401:mortality; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D012008:Recurrence; D016019:Survival Analysis