Bayesian vs. frequentist inference - an ophtalmic study on ocular perfusion pressure
Journal
International Scientific Journal Mathematical Modelling, Vol. 8 (2024), Issue 3
Date Issued
2024
Author(s)
Ljubic, Antonela
Dimitrova, Galina
Abstract
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.
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.