title: Generalized accelerated failure time spatial frailty model for arbitrarily censored data.
abstract: Flexible incorporation of both geographical patterning and risk effects in cancer survival models is becoming increasingly important, due in part to the recent availability of large cancer registries. Most spatial survival models stochastically order survival curves from different subpopulations. However, it is common for survival curves from two subpopulations to cross in epidemiological cancer studies and thus interpretable standard survival models can not be used without some modification. Common fixes are the inclusion of time-varying regression effects in the proportional hazards model or fully nonparametric modeling, either of which destroys any easy interpretability from the fitted model. To address this issue, we develop a generalized accelerated failure time model which allows stratification on continuous or categorical covariates, as well as providing per-variable tests for whether stratification is necessary via novel approximate Bayes factors. The model is interpretable in terms of how median survival changes and is able to capture crossing survival curves in the presence of spatial correlation. A detailed Markov chain Monte Carlo algorithm is presented for posterior inference and a freely available function frailtyGAFT is provided to fit the model in the R package spBayesSurv. We apply our approach to a subset of the prostate cancer data gathered for Louisiana by the surveillance, epidemiology, and end results program of the National Cancer Institute.
authors.affiliation: Division of Statistics, Northern Illinois University, DeKalb, IL, 60115, USA. zhouh@niu.edu.
authors.forename: Haiming
authors.initials: H
authors.lastname: Zhou
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, SC, 29208, USA.
authors.forename: Timothy
authors.initials: T
authors.lastname: Hanson
authors.affiliation: Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, 29208, USA.
authors.forename: Jiajia
authors.initials: J
authors.lastname: Zhang
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D011471:Prostatic Neoplasms / Q000401:mortality; D016019:Survival Analysis
keywords: Heteroscedastic survival; Interval-censored data; Linear dependent tailfree process; Spatial data; Stratified AFT model