“数理讲堂”2024年第9期:A threshold longitudinal Tobit quantile regression model for identification of treatment-sensitive subgroups based on interval-bounded longitudinal measurements and a continuous covariate

发布时间:2024-05-07 供稿:数理与统计学院 分享至:

主题:A threshold longitudinal Tobit quantile regression model for identification of treatment-sensitive subgroups based on interval-bounded longitudinal measurements and a continuous covariate

时间:05月09日 13:00-14:30

地点:腾讯会议:438-014-7163

主持人:姜荣 教授

报告人简介:

王占锋,中国科学技术大学统计与金融系副教授,应用统计专业硕士学位项目主管。分别于2003年和2008年获中国科学技术大学学士和理学博士学位。主要从事生物统计、函数型数据分析、非欧数据分析等领域的研究,在国内外学术期刊上发表论文60多篇。曾主持国家自然科学青年基金一项和面上基金两项,参与国家重点自然科学基金两项。全国工业统计学研究会数字经济与区块链技术协会秘书长,中国现场统计研究会资源与环境统计分会常务理事,中国现场统计研究会旅游大数据学会常务理事。

讲座简介:

Identification of a subgroup of patients who may be sensitive to a specific treatment is an important problem in precision medicine. This article considers the case where the treatment effect is assessed by longitudinal measurements, such as quality of life scores assessed over the duration of a clinical trial, and the subset is determined by a continuous baseline covariate, such as age and expression level of a biomarker. Recently, a linear mixed threshold regression model has been proposed but it assumes the longitudinal measurements are normally distributed. In many applications, longitudinal measurements, such as quality of life data obtained from answers to questions on a Likert scale, may be restricted in a fixed interval because of the floor and ceiling effects and, therefore, may be skewed. In this article, a threshold longitudinal Tobit quantile regression model is proposed and a computational approach based on alternating direction method of multipliers algorithm is developed for the estimation of parameters in the model. In addition, a random weighting method is employed to estimate the variances of the parameter estimators. The proposed procedures are evaluated through simulation studies and applications to the data from clinical trials.

打印
上一篇:下一篇: