Choudhary Pankaj K
University of Texas at Dallas, TX, USA.
Int J Biostat. 2010;6(1):Article 19. doi: 10.2202/1557-4679.1235.
We present a nonparametric methodology for evaluation of agreement between multiple methods of measurement of a continuous variable. Our approach is unified in that it can deal with any scalar measure of agreement currently available in the literature, and can incorporate repeated and unreplicated measurements, and balanced as well as unbalanced designs. Our key idea is to treat an agreement measure as a functional of the joint cumulative distribution function of the measurements from multiple methods. This measure is estimated nonparametrically by plugging-in a weighted empirical counterpart of the joint distribution function. The resulting estimator is shown to be asymptotically normal under some specified assumptions. A closed-form expression is provided for the asymptotic standard error of the estimator. This asymptotic normality is used to derive a large-sample distribution-free methodology for simultaneously comparing the multiple measurement methods. The small-sample performance of this methodology is investigated via simulation. The asymptotic efficiency of the proposed nonparametric estimator relative to the normality-based maximum likelihood estimator is also examined. The methodology is illustrated by applying it to a blood pressure data set involving repeated measurements from three measurement methods.
我们提出了一种用于评估连续变量多种测量方法之间一致性的非参数方法。我们的方法具有统一性，因为它可以处理文献中目前可用的任何一致性标量度量，并且可以纳入重复测量和非重复测量，以及平衡设计和非平衡设计。我们的关键思想是将一致性度量视为来自多种方法的测量值的联合累积分布函数的泛函。通过代入联合分布函数的加权经验对应物，对该度量进行非参数估计。在一些特定假设下，所得估计量被证明是渐近正态的。为估计量的渐近标准误差提供了一个封闭形式的表达式。这种渐近正态性被用于推导一种大样本无分布方法，用于同时比较多种测量方法。通过模拟研究了该方法的小样本性能。还检验了所提出的非参数估计量相对于基于正态性的最大似然估计量的渐近效率。通过将该方法应用于一个涉及三种测量方法重复测量的血压数据集来说明该方法。
