The separation of timescales in Bayesian survival modeling of the time-varying effect of a time-dependent exposure.
In this paper, we apply flexible Bayesian survival analysis methods to investigate the risk of lymphoma associated with kidney transplantation among patients with end-stage renal disease. Of key interest is the potentially time-varying effect of a time-dependent exposure: transplant status. Bayesian modeling of the baseline hazard and the effect of transplant requires consideration of 2 timescales: time since study start and time since transplantation, respectively. Previous related work has not dealt with the separation of multiple timescales. Using a hierarchical model for the hazard function, both timescales are incorporated via conditionally independent stochastic processes; smoothing of each process is specified via intrinsic conditional Gaussian autoregressions. Features of the corresponding posterior distribution are evaluated from draws obtained via a Metropolis-Hastings-Green algorithm.
Haneuse
Sebastien J-P A
SJ
Center for Health Studies, 1730 Minor Avenue, Suite 1600, Seattle, WA 98101-1448, USA. haneuse.s@ghc.org
Rudser
Kyle D
KD
Gillen
Daniel L
DL
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D006084:Graft Rejection / Q000401:mortality; D006085:Graft Survival; D006801:Humans; D007676:Kidney Failure, Chronic / Q000601:surgery; D016030:Kidney Transplantation / Q000473:pathology; D008223:Lymphoma / Q000209:etiology / Q000401:mortality*; D015233:Models, Statistical; D011183:Postoperative Complications / Q000503:physiopathology*; D018570:Risk Assessment / Q000379:methods; D016019:Survival Analysis; D015996:Survival Rate; D013995:Time