时 间：2024年1月10日 10:00-11:00
地 点：普陀校区理科大楼A302
报告人：Jeremy Taylor 密西根大学安娜堡分校教授
主持人：徐进 华东师范大学教授
摘 要：
We consider the setting where (i) an internal study builds a linear regression model for prediction based on individual-level data, (ii) some external studies have fitted similar linear regression models that use only subsets of the covariates and provide coefficient estimates for the reduced models without individual-level data, and (iii) there is heterogeneity across these study populations. The goal is to integrate the external model summary information into fitting the internal model to improve prediction accuracy. We adapt the James-Stein shrinkage method to propose estimators that have guaranteed improvement in the prediction mean squared error after information integration, regardless of the degree of study population heterogeneity. We conduct comprehensive simulation studies to investigate the numerical performance of the proposed estimators. We also apply the method to enhance a prediction model for patella bone lead level in terms of blood lead level and other covariates by integrating summary information from published literature.
报告人简介：
Jeremy M G Taylor PhD is the Pharmacia Professor of Biostatistics at the University of Michigan. He obtained a Bachelor’s degree in Mathematics and a Diploma in Statistics from Cambridge University and a PhD in Statistics from University of California Berkeley. He was a faculty member in the Department of Biostatistics and the Department of Radiation Oncology at UCLA from 1983 to 1998. He is currently a faculty member in the Department of Biostatistics, the Department of Radiation Oncology and the Department of Computational Medicine and Bioinformatics and the Director of the Center for Cancer Biostatistics at the University of Michigan. He is the winner of the Michael Fry award from the Radiation Research Society, the Mortimer Spiegelman award from the American Public Health Association, the Jerome Sacks award from the National Institute of Statistical Science and the Samuel Wilks award from the American Statistical Association. He is a former Chair of the Biometrics section of the American Statistical Association and a Fellow of the ASA. He is the former chair of the Biostatistical Methods and Research Design grant review committee for the National Institutes of Health. He was one of the coordinating editors of Biometrics from 2012-2014. He has over 400 publications and research interests in longitudinal and survival data, cure models, methods for missing data, causal inference, biomarkers, surrogate and auxiliary variables and data integration. He worked previously in AIDS research but currently mainly focusses on cancer research. He has served as the dissertation chair for 41 PhD students in Biostatistics at UCLA and the University of Michigan.