Tests for 2 x K contingency tables with clustered ordered categorical data
Tests for 2 x K contingency tables with clustered ordered categorical data
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DOI：
10.1002/sim.705
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发表时间：
2001-03-15
影响因子：
2
通讯作者：
Kang, SH
中科院分区：
文献类型：
--
作者：
Jung, SH;Kang, SH
在2 × K表中汇总的有序分类数据通常由两样本多项或K样本二项观测值组成。在分析这些数据时，我们通常为K列分配分数，并对前一种情况下两个多项式分布的相等性进行检验，而在后一种情况下K个二项比例之间没有趋势。最常用的分数检验是Wilcoxon秩和检验和Armitage线性趋势检验。在本文中，我们将分数检验扩展到不同研究设计下的聚类数据。我们的方法不需要正确规范集群内的依赖结构。所提出的检验是基于大量聚类的渐近正态性，并且是用于独立数据的标准检验的推广。仿真研究了新方法的有限样本性能。所提出的方法应用于实际数据。版权所有John Wiley & Sons， Ltd。
Ordered categorical data summarized in a 2 x K table usually consist of two-sample multinomial or K-sample binomial observations. In analysing these data, we usually assign scores to the K columns and perform a testing for the equality of two multinomial distributions in the former case and no trend among K binomial proportions in the latter case. Among the most popular score tests are the Wilcoxon rank sum test and the Armitage's linear trend test. In this paper we extend the score tests to be used for clustered data under diverse study designs. Our methods do not require correct specification of the dependence structure within clusters. The proposed tests are based on the asymptotic normality for large number of clusters and are a generalization of the standard tests used for independent data. Simulation studies are conducted to investigate the finite-sample performance of the new methods. The proposed methods are applied to real-life data. Copyright (C) 2001 John Wiley & Sons, Ltd.