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Estimation and regression with sequentially truncated survival data


数学与统计及交叉学科前沿论坛------高端学术讲座第120


报告题目:Estimation and regression with sequentially truncated survival data

报告人:Jing Qian, Ph.D., Associate Professor, Department of Biostatistics and Epidemiology, University of Massachusetts - Amherst

时间:2024623日(周日)16:00--17:00

地点:阜成路校区综合楼1116会议室


报告摘要:  In observational cohort studies with complex sampling schemes, truncation arises when the time to event of interest is observed only when it falls below or exceeds another random time, i.e., the truncation time. In more complex settings, observation may require a particular ordering of event times; we refer to this as sequential truncation. We propose nonparametric and semiparametric maximum likelihood estimators for the distribution of the event time of interest in the presence of sequential truncation, under two truncation models. We develop methods for regression modeling in this complex setting using the tool of pseudo-observations. We evaluate our approach in simulation studies and in application to an Alzheimer's cohort study.


报告人简介: Jing Qian 博士现在是马萨诸塞大学阿默斯特分校,公共卫生与健康科学学院生物统计学与流行病学系,生物统计学副教授(tenure)。他的研究兴趣包括复采样下的生存分析,他开发了观察性研究产生的删失和依赖截断下的估计和假设检验方法。通过开发复杂抽样设计下事件发生时间结果的分位数回归方法,Qian引入了灵活的建模方法来处理癌症流行病学研究中的非恒定生物标志物效应。目前,他还积极开发受删失影响的协变量的统计方法、复杂抽样设计下的风险预测、高维变量选择以及大规模成像或遗传生物标志物的预测。