The Statistics Seminar speaker for Wednesday, October 17, 2018, is Ding-Geng Chen. Dr. Chen received his Ph.D. in Statistics from University of Guelph (Canada) in 1995 and is now the Wallace H. Kuralt Distinguished Professor in Biostatistics, School of Social Work jointly as professor of biostatistics, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina-Chapel Hill. Dr. Chen was a biostatistics professor at the University of Rochester Medical Center, the Karl E. Peace endowed eminent scholar chair in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University. Dr. Chen is a fellow of American Statistical Association and a senior expert consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics. Dr. Chen has more than 150 scientific publications and co-authored/co-edited 23 books on clinical trials, survival data, meta-analysis, Monte-Carlo simulation-based statistical modelling, and statistical modelling for public health applications. His research has been funded as PI/Co-PI from NIH R01s.
Talk: Meta-Analysis Using Summary Statistics and Individual Participant-level Data
Abstract: Meta-analysis is a statistical methodology to combine information from diverse studies to reach a more reliable and efficient conclusion. It can be performed by either synthesizing study-level summary statistics (SS) or modeling individual participant-level data (IPD), if available. However, it remains not fully understood whether the use of IPD indeed gains additional efficiency over SS. In this talk, we discuss the relative efficiency of the two methods under a general likelihood inference setting. We show theoretically that there is no gain of efficiency asymptotically by analyzing IPD, provided that the random-effects follow the Gaussian distribution and maximum likelihood estimation is used to obtain summary statistics. Our findings are confirmed by simulation studies and a real data analysis of beta-blocker treatment effect for myocardial infarction. This is a joint work among Dungang Liu, Xiaoyi Min and Heping Zhang.