A NONPARAMETRIC MIXED-EFFECTS MIXTURE MODEL FOR PATTERNS OF CLINICAL MEASUREMENTS ASSOCIATED WITH COVID-19

• 成果类型: Article
• 署名作者: Ma, Xiaoran; Guo, Wensheg; Gu, Mengyang; Usvyat, Len; Kotanko, Peter; Wang, Yuedong
• 署名单位: University of California System; University of California Santa Barbara; University of Pennsylvania; Renal Research Institute
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1871
• 发表日期: 2024
• 页码: 2080-2095
• 关键词: functional data; Identifiability; prediction; algorithm

摘要

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.