On two-sample testing for data with arbitrarily missing values
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913278465146880 |
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| author | Zeng, Yijin Adams, Niall M. Bodenham, Dean A. |
| author_facet | Zeng, Yijin Adams, Niall M. Bodenham, Dean A. |
| contents | We develop a new rank-based approach for univariate two-sample testing in the presence of missing data which makes no assumptions about the missingness mechanism. This approach is a theoretical extension of the Wilcoxon-Mann-Whitney test that controls the Type I error by providing exact bounds for the test statistic after accounting for the number of missing values. Greater statistical power is shown when the method is extended to account for a bounded domain. Furthermore, exact bounds are provided on the proportions of data that can be missing in the two samples while yielding a significant result. Simulations demonstrate that our method has good power, typically for cases of $10\%$ to $20\%$ missing data, while standard imputation approaches fail to control the Type I error. We illustrate our method on complex clinical trial data in which patients' withdrawal from the trial lead to missing values. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15327 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On two-sample testing for data with arbitrarily missing values Zeng, Yijin Adams, Niall M. Bodenham, Dean A. Methodology 62 We develop a new rank-based approach for univariate two-sample testing in the presence of missing data which makes no assumptions about the missingness mechanism. This approach is a theoretical extension of the Wilcoxon-Mann-Whitney test that controls the Type I error by providing exact bounds for the test statistic after accounting for the number of missing values. Greater statistical power is shown when the method is extended to account for a bounded domain. Furthermore, exact bounds are provided on the proportions of data that can be missing in the two samples while yielding a significant result. Simulations demonstrate that our method has good power, typically for cases of $10\%$ to $20\%$ missing data, while standard imputation approaches fail to control the Type I error. We illustrate our method on complex clinical trial data in which patients' withdrawal from the trial lead to missing values. |
| title | On two-sample testing for data with arbitrarily missing values |
| topic | Methodology 62 |
| url | https://arxiv.org/abs/2403.15327 |