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Homogeneity Test for Correlated Binary Data

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  • Changxing Ma
  • Guogen Shan
  • Song Liu

Abstract

In ophthalmologic studies, measurements obtained from both eyes of an individual are often highly correlated. Ignoring the correlation could lead to incorrect inferences. An asymptotic method was proposed by Tang and others (2008) for testing equality of proportions between two groups under Rosner's model. In this article, we investigate three testing procedures for general g ≥ 2 groups. Our simulation results show the score testing procedure usually produces satisfactory type I error control and has reasonable power. The three test procedures get closer when sample size becomes larger. Examples from ophthalmologic studies are used to illustrate our proposed methods.

Suggested Citation

  • Changxing Ma & Guogen Shan & Song Liu, 2015. "Homogeneity Test for Correlated Binary Data," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    • Handle: RePEc:plo:pone00:0124337
    DOI: 10.1371/journal.pone.0124337
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    Cited by:

    1. Shuyi Liang & Kai-Tai Fang & Xin-Wei Huang & Yijing Xin & Chang-Xing Ma, 2024. "Homogeneity tests and interval estimations of risk differences for stratified bilateral and unilateral correlated data," Statistical Papers, Springer, vol. 65(6), pages 3499-3543, August.
    2. Zhiming Li & Changxing Ma & Mingyao Ai, 2020. "Statistical tests under Dallal’s model: Asymptotic and exact methods," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-18, November.

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