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Nonparametric Estimation of Distributions and Diagnostic Accuracy Based on Group-Tested Results with Differential Misclassification

发布时间：2020-10-30 作者： 浏览次数：
Speaker: 张维 DateTime: 2020年11月4日（周三）上午8:30-9:30
Brief Introduction to Speaker:

张维，现为中国科学院数学与系统科学研究院助理研究员。研究领域为生物医学统计，主要包括分组检测、临床试验、诊断医学及遗传关联分析等领域的统计理论、方法和应用。在期刊Biometrics、Statistics in Medicine、American Journal of Clinical Trials、Bioinformatics等发表论文20余篇。

 Place: 腾讯会议（会议号请联系李正帮老师索取）
Abstract:This article concerns the problem of estimating a continuous distribution in a diseased or non-diseased population when only group-based test results on the disease status are available. The problem is challenging in that individual disease statuses are not observed and testing results are often subject to misclassification, with further complication that the misclassification may be differential as the group size and the number of the diseased individuals in the group vary. We propose a method to construct nonparametric estimation of the distribution and obtain its asymptotic properties. The performance of the distribution estimator is evaluated under various design considerations concerning group sizes and classification errors. The method is exemplified with data from the National Health and Nutrition Examination Survey study to estimate the distribution and diagnostic accuracy of C-reactive protein in blood samples in predicting chlamydia incidence.
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