主 题：Merging longitudinal data when subject-level information is available
主讲人：Peter Song
主持人：林华珍教授
时 间：2013年12月12日上午10：30-11：30
地 点：通博楼B座212会议室
主办单位：统计学院 统计研究中心 科研处
主讲人简介：
Dr. Peter Song is currently Full Professor of Biostatistics in the School of Public Health, University of Michigan, Ann Arbor. He received his PhD degree in Statistics from University of British Columbia, Vancouver in 1996.Prior to the appointment of professorship at University of Michigan in Ann Arbor,he served as faculty in University of Waterloo and York University in Canada. Dr. Song is an elected member of International Statistical Institute, and the recipient of John von Neumann award from Technical University of Munich, Germany in 2013.He is now Associate Editor of Statistica Sinica, Canadian Journal of Statistics, Sankhya, and a Co-Guest Editor of Statistics and Its Interface. His current research is funded by multiple grants from National Science Foundation and National Institute of Health. Dr. Song has published over 80 articles in top-tier statistical and biomedical journals, including a single-authored monograph "Correlated Data Analysis: Modeling, Analytics and Applications" by Springer. Some of his published works have been reported by CNN, the Wall Street Journal and CBS.
内容提要：
In the context of meta analysis, when an access to subject-level data sets from multiple longitudinal studies is given, different strategies of combining information may be considered. It is possible to merge study-specific estimates, or study-specific estimating functions, or data sets. The currently popular strategy is to combine estimates, and a retreat of this classical approach will be given in the talk.As an alternative, a new strategy of combining estimating functions is proposed and compared in detail. Examples will be used to illustrate pros and cons of individual strategies for information aggregation.
