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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.00523 |
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| _version_ | 1866918149377490944 |
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| author | Liang, Shuyi Emura, Takeshi Ma, Chang-Xing Xin, Yijing Huang, Xin-Wei |
| author_facet | Liang, Shuyi Emura, Takeshi Ma, Chang-Xing Xin, Yijing Huang, Xin-Wei |
| contents | Handling highly dependent data is crucial in clinical trials, particularly in fields related to ophthalmology. Incorrectly specifying the dependency structure can lead to biased inferences. Traditionally, models rely on three fixed dependence structures, which lack flexibility and interpretation. In this article, we propose a framework using a more general model -- copulas -- to better account for dependency. We assess the performance of three different test statistics within the Clayton copula setting to demonstrate the framework's feasibility. Simulation results indicate that this method controls type I error rates and achieves reasonable power, providing a solid benchmark for future research and broader applications. Additionally, we present analyses of two real-world datasets as case studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00523 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Testing the Homogeneity of Proportions for Correlated Bilateral Data via the Clayton Copula Liang, Shuyi Emura, Takeshi Ma, Chang-Xing Xin, Yijing Huang, Xin-Wei Methodology Handling highly dependent data is crucial in clinical trials, particularly in fields related to ophthalmology. Incorrectly specifying the dependency structure can lead to biased inferences. Traditionally, models rely on three fixed dependence structures, which lack flexibility and interpretation. In this article, we propose a framework using a more general model -- copulas -- to better account for dependency. We assess the performance of three different test statistics within the Clayton copula setting to demonstrate the framework's feasibility. Simulation results indicate that this method controls type I error rates and achieves reasonable power, providing a solid benchmark for future research and broader applications. Additionally, we present analyses of two real-world datasets as case studies. |
| title | Testing the Homogeneity of Proportions for Correlated Bilateral Data via the Clayton Copula |
| topic | Methodology |
| url | https://arxiv.org/abs/2502.00523 |