Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models - Ebrahim, Endris Assen; Cengiz, Mehmet Ali (Frontiers in Psychology, 2022)
Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered to be a violation of the independence assumption in classical statistics. Such models are complex models for clustered data that account for distributions of hyper-parameters for fixed-term parameters in Bayesian computations. Repeated measures are inherently clustered and typically occur in clinical trials, education, cognitive psychology, and treatment follow-up. The Hopkins Verbal Learning Test (HVLT) is a general verbal knowledge and memory assessment that is administered repeatedly as part of a neurophysiological experiment to examine an individual's performance outcomes at different time points. Multiple trial-based scores of verbal learning and memory tests were considered as an outcome measurement. In this article, we attempted to evaluate the predicting effect of individual characteristics in considering within and between-group variations by fitting various Bayesian hierarchical models via hybrid Hamiltonian Monte Carlo (HMC) under the BRMS package of R. Comparisons of the fitted models were done using leave-one-out information criteria (LOO-CV), Watanabe applicable Bayesian information criterion (WAIC), and K-fold cross-validation methods. The full hierarchical model with varying intercepts and slopes had the best predictive performance for verbal learning tests (from the ACTIVE study dataset) using the hybrid Hamiltonian-Markov Chain Monte Carlo approach.