title: Modeling spatial survival data using semiparametric frailty models.
abstract: We propose a new class of semiparametric frailty models for spatially correlated survival data. Specifically, we extend the ordinary frailty models by allowing random effects accommodating spatial correlations to enter into the baseline hazard function multiplicatively. We prove identifiability of the models and give sufficient regularity conditions. We propose drawing inference based on a marginal rank likelihood. No parametric forms of the baseline hazard need to be assumed in this semiparametric approach. Monte Carlo simulations and the Laplace approach are used to tackle the intractable integral in the likelihood function. Different spatial covariance structures are explored in simulations and the proposed methods are applied to the East Boston Asthma Study to detect prognostic factors leading to childhood asthma.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. yili@jimmy.harvard.edu
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
authors.forename: Louise
authors.initials: L
authors.lastname: Ryan
mesh_terms: D001249:Asthma / Q000453:epidemiology / Q000209:etiology; D001699:Biometry; D001900:Boston / Q000453:epidemiology; D002648:Child; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009010:Monte Carlo Method; D011379:Prognosis; D016016:Proportional Hazards Models; D012307:Risk Factors; D016019:Survival Analysis