A Bayesian piecewise survival cure rate model for spatially clustered data. (June 2019)

• Record Type: Journal Article
• Authors: Hurtado Rúa, Sandra M.; Dey, Dipak K.
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 29(2019)
• Page Start: 149
• Page End: 159
• Publication Date: 2019-06
• Subjects: Bayesian hierarchical models -- Cure rate models -- Multivariate Conditional Autoregressive models -- Piecewise hazard functions -- Spatial correlation -- Survival analysis; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2019.02.001
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: We propose a unified model that estimates cure rates, spatial frailty and piecewise baseline hazards in one framework. We consider a mixture cure rate model with covariates. The baseline hazard is a piecewise function with a random join-point grid and fixed number of join-points. The frailty structure accounts for both spatial correlation and correlation between cure rates and survival functions. Our estimation methodology is Bayesian.We use independent non informative priors for the parameters or hyper-parameters. Abstract: This paper proposes a Bayesian hierarchical cure rate survival model for spatially clustered time to event data. We consider a mixture cure rate model with covariates and a flexible (semi)parametric baseline survival distribution for uncured individuals. The spatial correlation structure is introduced in the form of frailties which follow a Multivariate Conditionally Autoregressive distribution on a pre-specified map. We obtain the usual posterior estimates, smoothed by regional level maps of spatial frailties and cure rates. A simulation study demonstrates that the parameters of the models with spatially correlated frailties have smaller relative biases and MSE than the ones obtained using simple frailty models. We apply our methodology to Hodgkin lymphoma cancer survival times for patients diagnosed in the state of Connecticut.