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Goodness-of-fit Tests for Correlated Bilateral Data from Multiple Groups

In: Contemporary Experimental Design, Multivariate Analysis and Data Mining

Author

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  • Xiaobin Liu

    (University at Buffalo, Department of Biostatistics)

  • Chang-Xing Ma

    (University at Buffalo, Department of Biostatistics)

Abstract

Correlated bilateral data often arise in ophthalmological and otolaryngological studies, where responses of paired body parts of each subject are measured. A number of statistical methods have been proposed to tackle this intra-class correlation problem, and in practice it is important to choose the most suitable one which fits the observed data well. Tang et al. (Stat Methods Med Res 21(4):331–345, 2012, [16]) compared different goodness-of-fit statistics for correlated data including only two groups. In this article, we investigate the general situation for $$g\ge 2$$ groups. Our simulation results show that the performance of the goodness-of-fit test methods, as measured by the power and the type I error rate, is model depending. The observed performance difference is more significant in scenario with small sample size and/or highly dependent data structure. Examples from ophthalmologic studies are used to illustrate the application of these goodness-of-fit test methods.

Suggested Citation

  • Xiaobin Liu & Chang-Xing Ma, 2020. "Goodness-of-fit Tests for Correlated Bilateral Data from Multiple Groups," Springer Books, in: Jianqing Fan & Jianxin Pan (ed.), Contemporary Experimental Design, Multivariate Analysis and Data Mining, chapter 0, pages 311-327, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-46161-4_20
    DOI: 10.1007/978-3-030-46161-4_20
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