Informativeness of Diagnostic Marker Values and the Impact of Data Grouping.
Informativeness of Diagnostic Marker Values and the Impact of Data Grouping.
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诊断标记值的信息性和数据分组的影响。
DOI：
10.1016/j.csda.2017.07.008
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发表时间：
2018
影响因子：
1.8
通讯作者：
Gur,David
中科院分区：
文献类型：
--
作者：
Ma,Hua;Bandos,AndriyI;Gur,David
评估诊断标记物的性能是其在诊断医学和其他领域的各种感兴趣的条件的决策中使用的必要步骤。然而，全局有用的标记可能具有“诊断上无信息”的值范围。本文证明，诊断性非信息范围内标记值的存在可能会导致非参数评估过程中统计效率的损失，并表明对非信息值进行分组可以自然地解决此问题。这些观点在理论上得到了证明，并进行了广泛的模拟研究，以说明在许多实际合理的场景中使用分组标记值的可能好处。结果与关于绩效评估期间分组标记值的有害影响的常见猜想相矛盾。具体而言，与分组标记值会导致偏差的常见假设相反，对非信息值进行分组不会引入偏差，并且可以大大减少采样变异性。已证实的概念是，分组标记值在统计上可能是有益的，而不会产生有害后果，这意味着在实践中，捆绑值并不总是需要分辨率，而使用连续诊断结果而不解决诊断上的非信息范围可能在统计上是有害的。基于这些发现，可以开发更有效的评估诊断标记物的方法。
Assessing performance of diagnostic markers is a necessary step for their use in decision making regarding various conditions of interest in diagnostic medicine and other fields. Globally useful markers could, however, have ranges of values that are “diagnostically non-informative”. This paper demonstrates that the presence of marker values from diagnostically non-informative ranges could lead to a loss in statistical efficiency during nonparametric evaluation and shows that grouping non-informative values provides a natural resolution to this problem. These points are theoretically proven and an extensive simulation study is conducted to illustrate the possible benefits of using grouped marker values in a number of practically reasonable scenarios. The results contradict the common conjecture regarding the detrimental effect of grouped marker values during performance assessments. Specifically, contrary to the common assumption that grouped marker values lead to bias, grouping non-informative values does not introduce bias and could substantially reduce sampling variability. The proven concept that grouped marker values could be statistically beneficial without detrimental consequences implies that in practice, tied values do not always require resolution whereas the use of continuous diagnostic results without addressing diagnostically non-informative ranges could be statistically detrimental. Based on these findings, more efficient methods for evaluating diagnostic markers could be developed.