Bayesian inference for spatial parametric proportional hazards model using Spatsurv R

In many fields of applied studies, there has been increasing interest in developing and implementing Bayesian statistical methods for modelling and analysing the time-to-event data. Modelling the time-to-event data can be done by using the survival methods. In the survival data, we can find a set of complete records, in which the event and the survival time is known; and a set of censored records, in which the event was known to have occurred in an interval, but the survival time is unknown exactly. The time of an event frequently depends on the location; calls as spatial survival. In this paper, we performed a simulation study to estimate the spatial survival model parameters. The study was carried out with two different simulated datasets with right censored, which is, simulated assuming an exponential baseline hazard and a Weibull baseline hazard. The parametric proportional hazards model in which spatially correlated frailties with a log-Gaussian is the specific type of spatial model which is used. This model fitted by using the Spatsurv R package which implements a Markov Chain Monte Carlo (MCMC) algorithm to carry Bayesian inference. The result shows that the Spatsurv R package allows the user to construct and change their baseline hazard, the spatial covariance functions, the survival function plot and the baseline hazard function plot. The package functions are also able to cope with right censored survival data.