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.