Modeling Parkinson's Disease Progression Using Longitudinal Voice Biomarkers: A Comparative Study of Statistical and Neural Mixed-Effects Models

TL;DR

This study compares statistical and neural mixed-effects models for analyzing longitudinal voice data to monitor Parkinson's disease progression, highlighting the strengths of GAMMs in small-sample settings.

Contribution

It demonstrates that GAMMs outperform neural mixed-effects models in small datasets for Parkinson's progression prediction, emphasizing interpretability and predictive accuracy.

Findings

GAMMs achieved the lowest prediction error (MSE 6.56).

Neural mixed-effects models tend to overfit in small samples.

Larger cohorts are needed for neural models to be reliable.

Abstract

Longitudinal voice biomarkers provide a non-invasive source of information for monitoring Parkinson's disease progression, but their statistical analysis is difficult because repeated measurements from the same subject are correlated, clinical cohorts are often small, and disease trajectories can vary substantially across individuals. This study evaluates statistical and neural mixed-effects approaches for modeling Parkinson's disease progression from telemonitoring voice data. Using the Oxford Parkinson's telemonitoring dataset (N=42), we compare Neural Mixed Effects (NME) models, Generalized Neural Network Mixed Models (GNMMs), and semi-parametric Generalized Additive Mixed Models (GAMMs) under the same longitudinal prediction setting. The results show that neural mixed-effects models provide flexible nonlinear representations but can overfit severely in this small-sample setting, whereas GAMMs achieve stronger predictive performance and retain interpretable smooth effects and subject-level structure. In particular, the GAMM-based approach attains the lowest prediction error (MSE 6.56), while the neural baselines have substantially larger errors (MSE > 90). These findings support the use of interpretable statistical mixed-effects models for small longitudinal telemonitoring studies and suggest that larger and more diverse cohorts are needed before highly flexible neural mixed-effects models can be reliably assessed in this application.