报告题目：Improving marginal hazard ratio estimation using quadratic inference functions

报告时间：2023年06月30日（周五）上午9:30-11:00

报告链接：https://meeting.tencent.com/dm/YPdgMgGmbx3j

腾讯会议：134 148 496

报告摘要：Clustered and multivariate failure time data are commonly encountered in biomedical studies and a marginal regression approach is often employed to identify the potential risk factors of a failure. We consider a semiparametric marginal Cox proportional hazards model for right-censored survival data with potential correlation. We propose to use a quadratic inference function method based on the generalized method of moments to obtain the optimal hazard ratio estimators. The inverse of the working correlation matrix is represented by the linear combination of basis matrices in the context of the estimating equation. We investigate the asymptotic properties of the regression estimators from the proposed method. The optimality of the hazard ratio estimators is discussed. Our simulation study shows that the estimator from the quadratic inference approach is more efficient than those from existing estimating equation methods whether the working correlation structure is correctly specified or not. Finally, we apply the model and the proposed estimation method to analyze a study of tooth loss and have uncovered new insights that were previously inaccessible using existing methods.  

报告人简介：牛一，大连理工大学数学科学学院副教授，主要从事生存分析、统计计算以及与公共健康相关的交叉学科研究。在Statistics in Medicine，Journal of the National Cancer Institute，Statistical Methods in Medical Research，Lifetime Data Analysis等SCI期刊发表多篇学术论文，主持国家和省部级项目3项。