数学与统计学院学术报告[2023] 043号

(高水平大学建设系列报告814号)

报告题目:  A Nonparametric Mixed-Effects Mixture Model for Patterns of Clinical Measurements Associated with COVID-19

报告人： 王跃东教授（University of California, Santa Barbara）

报告时间：2023年   6 月 7日  （周三）下午 3:30—4:30

报告地点：汇星楼514

报告内容: 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.

报告人简历：王跃东博士，美国加州大学圣巴巴拉分校终身教授，是统计学界具有卓越贡献的研究者，为国际统计学院当选会士、美国统计学会当选会士、英国皇家学会会士，是国际数理统计协会、泛华统计协会、国际统计科学学会的会员。研究领域包括平滑样条、混合效应模型、生存分析、纵向数据、微阵列数据分析等方向。

欢迎师生参加！

报告邀请人：胡宗良  

                                    数学与统计学院

                                      2023年6月2日