International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This study investigates the application of Bayesian statistical methods for comparing ocular perfusion pressure (OPP) between glaucoma and non-glaucoma populations, contrasting it with traditional frequentist approaches. Using OPP measurements from two patient groups, we employ partially informed Bayesian models to test the hypothesis of no difference in means between the groups. We calculate Bayes factor using Savage-Dickey density ratio and offer insights in the hypothesis beyond p-values. The results highlight the advantages of the Bayesian approach, including its flexibility in incorporating prior information and interpreting evidence. We discuss the limitations and potential biases introduced by the choice of priors. This paper contributes to the understanding of Bayesian inference in ophthalmic research and emphasizes its potential for hypothesis testing in clinical studies.

This paper focuses on the simulation aspects of Bayesian hypothesis testing applied on ophthalmic data. Bayesian statistical analysis often relies on Markov Chain Monte Carlo (MCMC) methods for estimation, when analytical solutions are not possible. We highlight the key aspects of MCMC including model specification, details on the simulation, MCMC diagnostics as well as its limitations and advantages. The simulations are done using R and JAGS.