ABSTRACT Characterising the correspondence between an ordinal measurement and a continuous measurement is often of interest in mental health studies. To this end Peng et al. [(2011), ‘A Framework for Assessing Broad Sense Agreement Between Ordinal and Continuous Measurements’, Journal of the American Statistical Association, 106, 1592–1601] introduced the concept of broad sense agreement (BSA) and developed nonparametric estimation and inference for a BSA measure. In this work, we propose a nonparametric regression framework for BSA, which provides a robust tool to further investigate population heterogeneity in BSA. We develop inferential procedures including regression function estimation and hypothesis testing. Extensive simulation studies demonstrate satisfactory performance of the proposed method. We also apply the new method to a recent Grady Trauma Study and reveal an interesting impact of depression severity on the alignment between a self-reported symptom instrument and clinician diagnosis in posttraumatic stress disorder patients.

中文翻译：

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广义一致性的非参数回归方法

摘要 在心理健康研究中，表征有序测量和连续测量之间的对应关系通常很有趣。为此，彭等人。[(2011), 'A Framework for Assessing Broad Sense Agreement between Ordinal and Continuous Measurements', Journal of the American Statistical Association, 106, 1592–1601] 介绍了广义一致性 (BSA) 的概念并开发了非参数估计和推理BSA 措施。在这项工作中，我们为 BSA 提出了一个非参数回归框架，它为进一步研究 BSA 中的种群异质性提供了一个强大的工具。我们开发了推理程序，包括回归函数估计和假设检验。大量的模拟研究证明了所提出方法的令人满意的性能。