Peng Limin, Li Ruosha, Guo Ying, Manatunga Amita
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322 (
J Am Stat Assoc. 2011 Jan 1;106(496):1592-1601. doi: 10.1198/jasa.2011.tm10483. Epub 2012 Jan 24.
Conventional agreement studies have been confined to addressing the sense of reproducibility, and therefore are limited to assessing measurements on the same scale. In this work, we propose a new concept, called "broad sense agreement," which extends the classical framework of agreement to evaluate the capability of interpreting a continuous measurement in an ordinal scale. We present a natural measure for broad sense agreement. Nonparametric estimation and inference procedures are developed for the proposed measure along with theoretical justifications. We also consider longitudinal settings which involve agreement assessments at multiple time points. Simulation studies have demonstrated good performance of the proposed method with small sample sizes. We illustrate our methods via an application to a mental health study.
传统的一致性研究一直局限于解决可重复性的概念，因此仅限于评估同一尺度上的测量。在这项工作中，我们提出了一个新的概念，称为“广义一致性”，它扩展了经典的一致性框架，以评估在有序尺度上解释连续测量的能力。我们提出了一种广义一致性的自然度量。针对所提出的度量，开发了非参数估计和推断程序，并给出了理论依据。我们还考虑了纵向设置，其中涉及多个时间点的一致性评估。模拟研究表明，所提出的方法在小样本量时具有良好的性能。我们通过应用于一项心理健康研究来说明我们的方法。
