哪一种最优截止确定方法更适合于有序响应数据?模拟研究

A. Alkan, S. Yüksel, P. Demır
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摘要

摘要:本文研究了连续响应诊断试验中常用的最优截止方法在有序响应诊断试验中的性能。诊断准确性研究检查诊断测试区分患有和不患有该疾病的患者的能力。对于具有连续反应的诊断测试,计算可区分患者和健康个体的最佳截止点在实践中很重要。文献中提出了许多方法来获得连续试验结果的最佳切割点值。优登指数、ROC平面法中最接近-(0,1)角的点、一致性概率和最小p值法是确定最优切割点的常用方法。但文献中对这些方法在有序响应测试条件下的性能研究较少。因此,我们以样本量和群体平衡为模拟条件,通过模拟设计的方式,对上述几种有序响应数据的最优截止方法进行比较。患病组和非患病组的样本量为平衡设计(50,50)、(100,100)和(200,200),非平衡设计为(50,100)、(50,150)和(50,200)。对于每个场景,生成了1000个重复。计算了估计截止点与真实截止点(偏差)之间的差异。所有这些方法都高估了真实分界点,但方法的中位数偏差各不相同。对于非平衡设计,同样的结果是相关的,但对于平衡设计,最小p值方法的中位偏差为0,而其他方法的中位偏差为1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Which of the favorite optimal cut-off determination methods is preferable for the ordinal response data? A simulation study
Abstract The aim of this study is to investigate the performance of the optimal cut-off methods, which are generally used for the diagnostic tests with the continuous response, for the tests with the ordinal response. Diagnostic accuracy studies examine the ability of a diagnostic test to discriminate between the patients with and without the condition. For diagnostic tests with a continuous response, it is important in practice to calculate the optimal cut-off point that can differentiate patients and healthy individuals. There are many methods proposed in the literature to obtain the optimal cut-point value for continuous test results. The Youden index, the point closest-to-(0, 1) corner in the ROC plane approach, the concordance probability, and the minimum P-value approach are commonly used methods to determine optimal-cut-point. But the researches examining the performance of these methods in the setting of the ordinal response tests are lacking in the literature. So, we compared the mentioned optimal cut-off methods for the ordinal response data by the way of simulation design by considering the sample size and the balance of groups as simulation conditions. The sample sizes of the diseased and non-diseased group were set (50, 50), (100, 100), and (200, 200) for balanced design and (50, 100), (50, 150) and (50, 200) for unbalanced design. For each scenario, 1000 repeats were generated. The differences between the estimated and the true cut-off points (biases) were calculated. All these methods overestimated the true cut-off point, but the median biases of the methods were varying. For the unbalanced design, the same result was relevant but for the balanced design, the minimum P-value approach had a median bias as 0 while others have 1.
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