Copula Functions for Spatial Survival Data Analysis

Many survival data analyses aim to assess the effect of different risk factors on survival time. in some studies, the survival times are correlated, and the dependence between survival times is related to their spatial locations. identifying and considering the dependence structure of data is essential in survival modeling. the copula functions are helpful tools for incorporating data dependencies. so, one may use these functions for modelling spatial survival data. this paper presents a model for spatial survival data by the gumbel-hougaard copula function. a two-stage estimator using a composite likelihood function is used to estimate regression and dependence parameters. a simulation study investigates the performance of the model. finally, the proposed model is applied to model a set of covid-19 data.