“中国人民大学国际统计论坛”创办于2004年，致力于搭建统计学界高层次的学术交流平台，已成为中国最有影响力的统计学论坛之一。

2023年7月14日至15日，本届论坛将邀请5位主题报告人和6位特邀报告人，本次介绍特邀报告人蔡天西，预祝第九届中国人民大学国际统计论坛取得圆满成功！

蔡天西

Title

Generating Robust Evidence with Multi-institutional EHR Data

Abstract

While clinical trials and cohort studies remain critical sources for studying disease progression and treatment response, they have limitations including the generalizability of the study findings to the real world, the limited ability to examine subgroup effects or test broader hypotheses, and the cost in performing these studies. In recent years, due to the increasing adoption of electronic health records (EHR) and the linkage of EHR with specimen bio-repositories and other research registries, integrated large datasets now open opportunities to generate real-world evidence (RWE). Generating reliable RWE with EHR studies, however, remain highly challenging due to heterogeneity across healthcare centers in their patient population and health dynamics. In addition, sharing detailed patient-level data across institutions remains infeasible due to privacy constraints. In this talk, I will discuss federated approaches to generating RWE using multi-institutional EHR data.

Biography

Tianxi Cai, ScD, is John Rock Professor of Population and Translational Data Sciences and Professor of Bioinformatics at Harvard T.H. Chan School of Public Health, and Professor of Biomedical Informatics at Harvard Medical School. Dr. Cai is a major player in developing analytical tools for mining EHR data and predictive modeling with biomedical data. She directs the HMS and HSPH translational data science center for a learning health system. Cai’s research lab develops novel statistical and machine learning methods for several areas including clinical trials, real world evidence, and personalized medicine using genomic and phenomic data. Cai received her ScD in Biostatistics at Harvard and was an assistant professor at the University of Washington before returning to Harvard as a faculty member in 2002.