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

Xiaobin Liu () and Chang-Xing Ma ()
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Xiaobin Liu: University at Buffalo, Department of Biostatistics
Chang-Xing Ma: University at Buffalo, Department of Biostatistics

Chapter Chapter 20 in Contemporary Experimental Design, Multivariate Analysis and Data Mining, 2020, pp 311-327 from Springer

Abstract: 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-46161-4_20

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DOI: 10.1007/978-3-030-46161-4_20

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