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存在检测误差时用于病例识别的分组检测算法比较。

Comparison of group testing algorithms for case identification in the presence of test error.

作者信息

Kim Hae-Young, Hudgens Michael G, Dreyfuss Jonathan M, Westreich Daniel J, Pilcher Christopher D

机构信息

Department of Biostatistics, School of Public Health, University of North Carolina at Chapel Hill, 3107-E McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599, USA.

出版信息

Biometrics. 2007 Dec;63(4):1152-63. doi: 10.1111/j.1541-0420.2007.00817.x. Epub 2007 May 14.

Abstract

We derive and compare the operating characteristics of hierarchical and square array-based testing algorithms for case identification in the presence of testing error. The operating characteristics investigated include efficiency (i.e., expected number of tests per specimen) and error rates (i.e., sensitivity, specificity, positive and negative predictive values, per-family error rate, and per-comparison error rate). The methodology is illustrated by comparing different pooling algorithms for the detection of individuals recently infected with HIV in North Carolina and Malawi.

摘要

我们推导并比较了在存在检测误差的情况下用于病例识别的分层和基于方阵的检测算法的操作特性。所研究的操作特性包括效率(即每个样本的预期检测次数)和错误率(即灵敏度、特异性、阳性和阴性预测值、家族错误率和每次比较错误率)。通过比较北卡罗来纳州和马拉维用于检测近期感染艾滋病毒个体的不同混合算法来说明该方法。

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