An Integrated Bayesian Nonparametric Approach for Stochastic and Variability Orders in ROC Curve Estimation: An Application to Endometriosis Diagnosis
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同时纳入随机序与变异序的贝叶斯非参数 ROC 曲线估计
- 研究问题
- 估计多个诊断测试的 ROC 曲线时，如何同时利用健康与患病群体之间的随机序约束，以及不同测试分布离散程度的顺序约束？
- 方法与思路
- 作者采用 Dirichlet 过程混合模型纳入两类顺序约束，并在子宫内膜异位症诊断应用中，利用专家组诊断开展无金标准敏感性分析。
- 材料陈述的贡献
- 摘要称，方法将随机序与变异序统一纳入 ROC 建模，并通过不同样本量、分布设定和顺序约束下的模拟及诊断数据展示用途。
- 阅读关注点
- 应核对两类顺序的严格定义、约束如何嵌入混合模型，以及专家组诊断的作用和无金标准敏感性分析覆盖的情形。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，无法判断顺序约束是否符合实际数据，也不能确认诊断准确率或敏感性结论；刊本变化未核验。
原文摘要
Beom Seuk Hwang & Zhen ChenBeom Seuk Hwang is Postdoctoral Fellow (E-mail: ) and Zhen Chen is Investigator (E-mail: ), Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892. This research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health. This study used the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD (http://biowulf.nih.gov). The authors thank Athanasios Kottas, Paul S. Albert, the editor, associate editor, and two anonymous reviewers for insightful and helpful comments.In estimating ROC curves of multiple tests, some a priori constraints may exist, either between the healthy and diseased populations within a test or between tests within a population. In this article, we proposed an integrated modeling approach for ROC curves that jointly accounts for stochastic and variability orders. The stochastic order constrains the distributional centers of the diseased and healthy populations within a test, while the variability order constrains the distributional spreads of the tests within each of the populations. Under a Bayesian nonparametric framework, we used features of the Dirichlet process mixture to incorporate these order constraints in a natural way. We applied the proposed approach to data from the Physician Reliability Study that investigated the accuracy of diagnosing endometriosis using different clinical information. To address the issue of no gold standard in the real data, we used a sensitivity analysis approach that exploited diagnosis from a panel of experts. To demonstrate the performance of the methodology, we conducted simulation studies with varying sample sizes, distributional assumptions, and order constraints. Supplementary materials for this article are available online.