A nonparametric mixed-effects mixture model for patterns of clinical measurements associated with COVID-19
Year: 2024
Type: article
Abstract: Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insights into how the immune system may respond differently to infection and can further be used to identify infected individuals. We propose a flexible nonparametric mixed-effects mixture model that identifies risk factors and classifies patients with biological changes. We model the latent probability of biological changes using a logistic regression model and trajectories in the latent groups using smoothing splines. We developed an EM algorithm to maximize the penalized likelihood for estimating all parameters and mean functions. We evaluate our methods by simulations and apply the proposed model to investigate changes in temperature in a cohort of COVID-19-infected hemodialysis patients.
Source: The Annals of Applied Statistics
DOI: 10.1214/23-aoas1871
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: Semi-Parametric Subgroup Analysis for Longitudinal Data with Applications to Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Study, Early detection, containment, and management of COVID-19 in dialysis facilities using multi-modal data sources, Collaborative Research: Fusing Massive Disparate Data and Fast Surrogate Models for Probabilistic Quantification of Uncertain Hazards, Dynamic Longitudinal Functional Models with Applications to the CRIC Study
OpenAlex ID: W4401374136