Prediction intervals for random-effects meta-analysis: a confidence distribution approach
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2018
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| _version_ | 1866908649033564160 |
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| author | Nagashima, Kengo Noma, Hisashi Furukawa, Toshi A. |
| author_facet | Nagashima, Kengo Noma, Hisashi Furukawa, Toshi A. |
| contents | Prediction intervals are commonly used in meta-analysis with random-effects models. One widely used method, the Higgins-Thompson-Spiegelhalter prediction interval, replaces the heterogeneity parameter with its point estimate, but its validity strongly depends on a large sample approximation. This is a weakness in meta-analyses with few studies. We propose an alternative based on bootstrap and show by simulations that its coverage is close to the nominal level, unlike the Higgins-Thompson-Spiegelhalter method and its extensions. The proposed method was applied in three meta-analyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1804_01054 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Prediction intervals for random-effects meta-analysis: a confidence distribution approach Nagashima, Kengo Noma, Hisashi Furukawa, Toshi A. Methodology Prediction intervals are commonly used in meta-analysis with random-effects models. One widely used method, the Higgins-Thompson-Spiegelhalter prediction interval, replaces the heterogeneity parameter with its point estimate, but its validity strongly depends on a large sample approximation. This is a weakness in meta-analyses with few studies. We propose an alternative based on bootstrap and show by simulations that its coverage is close to the nominal level, unlike the Higgins-Thompson-Spiegelhalter method and its extensions. The proposed method was applied in three meta-analyses. |
| title | Prediction intervals for random-effects meta-analysis: a confidence distribution approach |
| topic | Methodology |
| url | https://arxiv.org/abs/1804.01054 |