Dr. Gang Li（Department of Biostatistics, UCLA）特別セミナー（ハイブリッド開催）

　本セミナーは終了しました。多くのご参加をいただきありがとうございました。

　統計数理研究所では、2024年6月17日（月）午後、UCLAのDr. Gang Liを招き、ハイブリッド形式で特別セミナーを開催します。
　Dr. Gang Liは現在、UCLAのDepartment of Biostatisticsの教授、また、UCLA Jonsson Comprehensive Cancer Center Biostatistics, Analytical Support and Evaluation (BASE) Shared ResourceのDirectorを務めており、Biostatisticsの世界において、精力的に研究教育活動を展開しています。Dr. Gang Liには初めての統計数理研究所来所となります。
　本特別セミナーでは、Dr. Gang Liによる講演の他、Q and Aセッション、フリーディスカッションの時間を設ける予定です。
　ご参加希望の方は、下記よりご登録ください。（※現地ご参加希望の方もご登録をお願いいたします。）
　★参加登録は2024年6月14日（金）正午までにお願いいたします★
　　　　https://us06web.zoom.us/webinar/register/WN_Z_f_x55ITT6ygDxXpaxNdQ 　
　

【開催日時】
2024年6月17日（月）15：45 ～17：30（予定）

【開催形式】ハイブリッド（会場およびZoomウェビナー）
会場：統計数理研究所３階セミナー室５（D313・314）
　　　　https://www.ism.ac.jp/access/index_j.html
【プログラム】
Speaker:
Dr. Gang Li (Professor, Department of Biostatistics, UCLA/Director, UCLA Jonsson Comprehensive Cancer Center Biostatistics, Analytical Support,and Evaluation (BASE) Shared Resource)
Time Table:
    ・ Talk by Dr. Gang Li (60 minutes)
   　 "Prediction Accuracy Measures for Nonlinear Models and Right-Censored Survival Data"
   ・ Q and A Session (30minutes)
   ・ Free Discussion (15 minutes)
※　本セミナーはすべて英語で行われます。
※　ウェビナーでの配信はQ and A Sessionまでとなります。
※　記録のため、本セミナーを録画させていただく場合がありますのでお含みおきください。

[Abstract]
Evaluating the performance of a prediction function is a fundamental task in statistics and machine learning. However, despite the availability of numerous prediction performance measures, there is no consensus on the best one to use for nonlinear models, especially when dealing with censored time-to-event data. In this talk, I will illustrate that many common prediction performance measures may fail to distinguish the prediction performance between different models using a well-known clinical trial data. I will then introduce a new pseudo R-squared statistic, which extends the classical R-squared statistic to any nonlinear prediction function and to right-censored time-to-event data. The pseudo R-squared statistic is obtained from a pair of orthogonal prediction performance measures based on a variance decomposition and a prediction error decomposition. Its effectiveness will be demonstrated using simulations and various real world data examples. Extension to time-dependent performance measures and competing risks data will also be discussed. An R package, PAmeasures, is available to implement these measures for various nonlinear and survival models.

[Biography]
Dr. Gang Li is a Professor of Biostatistics and Computational Medicine at the University of California, Los Angeles (UCLA). He also serves as Director of the UCLA Health Jonsson Comprehensive Cancer Center Biostatistics Shared Resource. Dr. Li is an Elected Fellow of the Institute of Mathematical Statistics, the American Statistical Association, and the Royal Statistical Society, as well as an Elected Member of the International Statistical Institute. Among the many significant roles he has held in the statistical profession, Dr. Li serves as the co-Editor-in-Chief (2022-2024) of the Electronic Journal of Statistics, published by the Institute of Mathematical Statistics and the Bernoulli Society. Additionally, he has served as the President (2022-2024) of the International Chinese Statistical Association. Dr. Li’s research encompasses a broad range of areas, including survival analysis, longitudinal data analysis, high-dimensional data analysis, clinical trials, statistical learning, causal inference, and high-performance statistical computing for large-scale electronic health records (EHR) and biobank data. He has made significant contributions to these fields, co-authored/edited three research monographs, and published over 150 peer-reviewed papers, many of which are featured in renowned journals such as the Annals of Statistics, the Journal of the American Statistical Association, and the Journal of the Royal Statistical Society-B. In addition to his methodological research, Dr. Li actively engages in collaborative research in basic science, translational science, and clinical trials. He has served as the Principal Investigator for numerous studies funded by the National Institutes of Health (NIH) and the National Science Foundation (NSF).

【主催】
統計数理研究所

【問い合わせ先】
統計数理研究所　副所長／教授　川崎能典（kawasaki@ism.ac.jp)
統計数理研究所　運営企画本部（uneikikaku-sec@grp.ism.ac.jp）
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