报告题目：Efficient algorithms for survival data with multiple outcomes using the frailty model
报告人：黄希芬(云南师范大学)
报告时间：2023年4月28日10：00-11:00
报告地点：腾讯会议ID:859-560-039
摘要: Survival data with multiple outcomes are frequently encountered in biomedical investigations. An illustrative example comes from Alzheimer’s Disease Neuroimaging Initiative study where the cognitively normal subjects may clinically progress to mild cognitive impairment and/or Alzheimer’s disease dementia. Transition time from normal cognition to mild cognitive impairment and that from mild cognitive impairment to Alzheimer’s disease are expected to be correlated within subjects and the dependence is often accommodated by the frailty (random effects). Estimation in the frailty model unavoidably involves multiple integrations which may be intractable and hence leads to severe computational challenges, especially in the presence of high-dimensional covariates. In this paper, we propose efficient minorization-maximization algorithms in the frailty model for survival data with multiple outcomes. The alternating direction method of multipliers is further incorporated for simultaneous variable selection and homogeneity pursuit via regularization and fusion. Extensive simulation studies are conducted to assess the performance of the proposed algorithms. An application to the Alzheimer’s Disease Neuroimaging Initiative data is also provided to illustrate their practical utilities.
主讲人简介：黄希芬，云南师范大学数学学院讲师，主要从事生物医学中统计模型和快速算法等方面的研究，主持国家自然科学基金项目2项，发表论文10余篇。2019年和2023年先后获得云南省“兴滇英才支持计划”青年人才和云南省社会科学奖等奖项。
