报告人：胡涛
报告地点：腾讯会议（#腾讯会议：765-820-752）
报告时间：2024年12月13日星期五10：00-11：00
报告摘要：
Group testing has been used extensively to reduce the testing time and the screening costs in epidemiological studies involving low-prevalence diseases. This testing strategy works by first combining specimens (e.g., blood, urine, swabs, etc.) from several individuals to form a pool and then testing the pooled specimen for infection. When the endpoint of interest is a time-to-event outcome, for example, the time until infection or disease, and pools are tested only once, the resulting data are called group-tested current status data (Petito and Jewell, 2016). In this paper, we propose a new type of regression analysis for these data using a semiparametric probit model, an alternative to the proportional hazards model used in survival analysis. A sieve maximum likelihood estimation approach is developed that approximates the model’s nonparametric nuisance function with logarithmic monotone splines. To facilitate sieve estimation, we develop a highly efficient expectation-maximization algorithm. The asymptotic properties of the resulting estimators are investigated by using empirical process techniques and sieve estimation theory. Numerical results from simulation studies suggest our proposed method performs nominally, even when pools are possibly misclassified due to assay error, and can outperform individual testing when the number of assays (tests) is fixed. We illustrate our work by estimating a time-to-event regression model for chlamydial infection using group testing data from a large public health laboratory in Iowa.
主讲人简介：
胡涛，首都师范大学数学科学学院教授，博士生导师。研究方向：生物统计、应用统计。在国内外学术刊物Journal of the American Statistical Association、Biometrika、Bioinformatics、Biometrics、Renewable Energy和中国科学：数学等上发表学术论文多篇。主持北京高校卓越青年科学家计划项目、国家自然科学基金面上项目、北京市自然科学基金重点研究专题等多项基金项目。
