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20190507 Yuan Huang: Promoting sign consistency in the cure model estimation and selection
时间:2019-04-30


报告时间:2019年5月7日 10:30-11:30

报告地点:明德主楼1030会议室

报告主题: Promoting sign consistency in the cure model estimation and selection


报告摘要:

In survival analysis, when a subset of subjects has extremely long survival, the two-part cure rate model has been commonly adopted. In the two-part model, the first part is for a binary response and describes the probability of cure. The second part is for a survival response and describes the probability of survival. Despite their intuitive interconnections, most of the existing works estimate the two parts without any constraint. The existing works on proportionality promote similarity in magnitudes (i.e. quantitative similarity) and can be too restrictive. In this study, for the two-part cure rate model, we propose imposing a sign-based penalty to promote similarity in signs (i.e. qualitative similarity). The proposed strategy can be more informative than those that neglect the two-part interconnections and be less restrictive than the existing proportionality works. Penalty is also imposed to select relevant variables and accommodate high-dimensional data. Numerical studies, including simulation and two data analyses, demonstrate the advantageous performance of the proposed approach


报告人简介:

Dr. Yuan Huang is an Assistant Professor of Biostatistics at the University of Iowa. She received her Ph.D. degree in Statistics at the Pennsylvania State University in 2015. Her research focuses on the development and application of statistical methods for high-dimensional data. Particular areas of interest include variable selection, high-dimensional hypothesis testing, and integrative analysis. Much of her work is motivated by cancer genomics data. Applications include pathway testing and marker identification associated with cancer etiology, prognosis, and progression, as well as large-scale network structure estimation. Beyond methodological research, she is actively involved in collaborative research on clinical trials, genetics, epidemiology, and other biomedical fields.