报告人：Zhengjun Zhang
报告地点：腾讯会议ID:972 477 521
报告时间：2022年09月13日星期二15:30-17:00
报告摘要：
Logistic regression is widely used in the analysis of medical data with binary outcomes to study treatment effects through (absolute) treatment effect parameters in the models. However, the indicative parameters of relative treatment effects are not introduced in logistic regression models, which can be a severe problem in efficiently modeling treatment effects and lead to the wrong conclusions with regard to treatment effects. This paper introduces a new enhanced logistic regression model that offers a new way of studying treatment effects by measuring the relative changes in the treatment effects and also incorporates the way in which logistic regression models the treatment effects. The new model, called the Absolute and Relative Treatment Effects (AbRelaTEs) model, is viewed as a generalization of logistic regression and an enhanced model with increased flexibility, interpretability, and applicability in real data applications than the logistic regression. The AbRelaTEs model is capable of modeling significant treatment effects via an absolute or relative or both ways. The new model can be easily implemented using statistical software, with the logistic regression model being treated as a special case. As a result, the classical logistic regression models can be replaced by the AbRelaTEs model to gain greater applicability and have a new benchmark model for more efficiently studying treatment effects in clinical trials, economic developments, and many applied areas. Moreover, the estimators of the coefficients are consistent and asymptotically normal under regularity conditions. In both simulation and real data applications (including Covid-19), the model provides both significant and more meaningful results.
主讲人简介：
Professor Zhengjun Zhang (Fellows, IMS, ASA) is the tenured full professor in the Department of Statistics at the University of Wisconsin –Madison. He has published over 100 journal research articles. His research interests include risk analysis, nonlinear time series, big data analytics, nonlinear and asymmetric causal inferences, stochastic optimization, high dimensional variable selection, intrinsic genomic interactions among cancers, infectious diseases, and absolute and relative treatments in clinical trials.
Dr. Zhang served as Associate Editors for Journal of Business and Economic Statistics, Statistics Sinica, Journal of Data Science, Electronic Journal of Statistics, and guest editors for Journal of Econometrics. He organized three international symposiums for financial engineering and risk management and many other workshops.
