Date of Award

2026

Document Type

Thesis (Ph.D.)

Department or Program

Quantitative Biomedical Sciences

First Advisor

Jiang Gui

Second Advisor

Jay C. Buckey Jr.

Abstract

Central auditory function is linked with cognitive deficits, but few research projects use existing statistical approaches or develop new ones to forecast cognitive deficits using the results of central auditory tests. To address this limitation, we use a series of statistical learning frameworks for predicting a child’s cognitive abilities based on his/her central auditory performances and demographic factors. Two key challenges exist. First, children may start the study at a time when they are unable to perform the central auditory tests or cognitive tests. Second, cognitive performance is age-dependent, particularly in the early formative years of childhood and adolescence. To address these challenges, we develop a statistical pipeline to account for age dependency as well as to retrieve optimal information on the central auditory and cognitive tests. Moreover, we formulate a set of novel preprocessing approaches of adjusting central auditory performances prior to predicting a concrete and consequential cognitive ability--reading performance. Interestingly, predictive performance differed across HIV subgroups despite no statistically significant differences in the distribution of the predictors and outcomes. This pattern suggests that the observed difference in the results is likely not due to simple characteristic differences at the HIV sub-group level but hints at the influence of latent longitudinal structures or underlying heterogeneity in the data. The final project makes use of simulated data to investigate the interplay between early detection of latent clusters and cross-temporal predictor-outcome relationships under various settings.

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