Abstract:
Survival analysis is a collection of statistical techniques which examine the time it takes for an event to occur, and it is one of the
most important fields in biomedical sciences and other variety of scientific disciplines. Furthermore, the computational rapid
advancements in recent decades have advocated the application of Bayesian techniques in this field, giving a powerful and flexible
alternative to the classical inference. The aim of this study is to consider the Bayesian inference for the generalized log-logistic
proportional hazard model with applications to right-censored healthcare data sets. We assume an independent gamma prior for
the baseline hazard parameters and a normal prior is placed on the regression coefficients. We then obtain the exact form of the
joint posterior distribution of the regression coefficients and distributional parameters. The Bayesian estimates of the parameters
of the proposed model are obtained using the Markov chain Monte Carlo (McMC) simulation technique. All computations are
performed in Bayesian analysis using Gibbs sampling (BUGS) syntax that can be run with Just Another Gibbs Sampling (JAGS)
from the R software. A detailed simulation study was used to assess the performance of the proposed parametric proportional
hazard model. Two real-survival data problems in the healthcare are analyzed for illustration of the proposed model and for model
comparison. Furthermore, the convergence diagnostic tests are presented and analyzed. Finally, our research found that the
proposed parametric proportional hazard model performs well and could be beneficial in analyzing various types of survival data.
