Using Hierarchical Bayesian Modeling to Enhance Statistical Inference on Contrast Sensitivity.

ABSTRACT:

Purpose

The purpose of this study is to introduce a nonparametric hierarchical Bayesian model (HBM) that enables advanced statistical inference on contrast sensitivity (CS) both at individual spatial frequencies (SFs) and across multiple SFs in clinical trials, where CS measurements are crucial for assessing safety and efficacy.

Methods

The HBM computes the joint posterior distribution of CS at six Food and Drug Administration-designated SFs across the population, individual, and test levels. It incorporates covariances at both population and individual levels to capture the relationship between CSs across SFs. A Bayesian inference procedure (BIP) is also used to estimate the posterior distribution of CS at each SF independently. Both methods are applied to a quantitative CS

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC11645744 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature