报告人：Chen Jinbo (陈金波教授，University of Pennsylvania)

报告题目：An Estimating Equation Approach to Adjusting for Case Contamination in Electronic Health Records-based Case-Control Studies

报告人简介：陈金波，宾夕法尼亚大学佩雷尔曼医学院生物统计学教授，研究方向为两阶段流行病学，复杂疾病遗传关联研究，风险预测模型，妊娠疾病和早期生活障碍的遗传环境因素研究等等。陈教授是多个国际学术期刊如《Biometrics》，《Cancer Epidemiology》，《Biomarkers and Prevention》等的副主编或编委，已在知名国际期刊发表了近百篇学术论文。

报告摘要：Clinically relevant information from electronic health records (EHRs) permits derivation of a rich collection of phenotypes. Unfortunately, the true status of any given individual with respect to a phenotype of interest is not necessarily known. A common study design is to use structured clinical data elements to identify case and control groups. While controls can usually be identified with high accuracy through rigorous selection criteria, the stringency of rules for identifying cases needs to be balanced against the achievable sample size. The inaccurate identification results in a pool of candidate cases consisting of genuine cases and non-case subjects that do not satisfy control definition. This case contamination issue represents a unique challenge in EHR-based case-control studies. We propose a novel estimating equation (EE) approach to estimating odds ratio association parameters and study its large sample properties. We evaluate the large and finite sample performance of our method through extensive simulation studies and application to a real EHR-based study of aortic stenosis. A practical issue for designing EHR-based case-control studies is the balance between accuracy and size of the case pool. Our simulation results showed that enlarging the case pool by incorporating more genuine cases can lead to improved statistical efficiency of EE estimates.

报告时间：2018年9月24日（星期一）上午10：30 - 11：30.

报告地点：科技楼南楼702