Dr. Ming Wang gave a lecture at AMSS at AMSS on 23, December 2020.

Recurrent events could be stopped by a terminal event, which commonly occurs in biomedical and clinical studies. Taking the Cardiovascular Health Study (CHS) as a motivating example, patients can experience recurrent events of myocardial infarction (MI) or stroke during follow-up, which, however, can be truncated by death. Since death could be a devastating complication of myocardial infarction or stroke recurrences, ignoring dependent censoring when analyzing recurrent events may lead to invalid inference. The joint frailty model is widely used but with several limitations, such as the assumption of conditional independence, constant correlation between recurrent events and death and so on. Recently, we proposed joint frailty-copula models and also later extended this work by incorporating time-varying dependency between these event processes for more valid and informative inference. In this talk, they not only introduce these methods, but also present potential future directions with emphasis on predicted accuracy assessment. Extensive simulation studies are performed for method evaluation and comparisons with the other alternative approaches, and lastly the application of the CHS study is provided for illustration.

Dr. Wang is a tenured Associate professor in the Division of Biostatistics and Bioinformatics, Department of Public Health Sciences (PHS) at the Penn State College of Medicine. Before joining the Penn State College of Medicine in 2013, Dr. Wang received Ph.D. degree in Biostatistics from Department of Biostatistics and Bioinformatics at Emory University, and a bachelor degree in Applied Mathematics from Peking University in China. Her research interests include longitudinal data analysis, survival analysis, spatial statistics, high-dimensional data analysis and other (bio) statistical aspects related to biomedical and human health research.