A sequential classification rule based on multiple quantitative tests in the absence of a gold standard.
A sequential classification rule based on multiple quantitative tests in the absence of a gold standard.
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在没有金标准的情况下基于多次定量测试的顺序分类规则。
DOI：
10.1002/sim.6780
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发表时间：
2016
影响因子：
2
通讯作者：
Stapleton,JackT
中科院分区：
文献类型：
--
作者：
Zhang,Jingyang;Zhang,Ying;Chaloner,Kathryn;Stapleton,JackT
在许多医学应用中，结合来自多个生物标记物的信息可以产生比任何单个单独的诊断更好的诊断。在缺乏金标准的情况下，需要一种区分案例和非案例状态的算法来组合多个标记物。本文的目的是发展一种方法，从多个适用的测试中构造一个复合测试，并在没有黄金标准的情况下推导出一个最优分类规则。我们不是将测试组合在一起，而是将测试视为一个序列。这种序贯复合检验基于两个多元正态潜在模型的混合，用于在病例组和非病例组中分布测试结果，并推导出在给定的特异度下返回最大灵敏度的最优分类规则。将该方法应用于一个实际数据实例，并进行了仿真研究，以评估所提出的复合检验的统计特性和预测精度。这种方法也可以实现非参数实现。版权所有©2015 John Wiley&Sons，Ltd.
In many medical applications, combining information from multiple biomarkers could yield a better diagnosis than any single one on its own. When there is a lack of a gold standard, an algorithm of classifying subjects into the case and non‐case status is necessary for combining multiple markers. The aim of this paper is to develop a method to construct a composite test from multiple applicable tests and derive an optimal classification rule under the absence of a gold standard. Rather than combining the tests, we treat the tests as a sequence. This sequential composite test is based on a mixture of two multivariate normal latent models for the distribution of the test results in case and non‐case groups, and the optimal classification rule is derived returning the greatest sensitivity at a given specificity. This method is applied to a real‐data example and simulation studies have been carried out to assess the statistical properties and predictive accuracy of the proposed composite test. This method is also attainable to implement nonparametrically. Copyright © 2015 John Wiley & Sons, Ltd.