Goodness-of-fit testing for meta-analysis of rare binary events
Abstract Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were proposed to assess the fit of RE model. However, they tend to perform poorly when assessing...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-44638-x |
| _version_ | 1827634604503203840 |
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| author | Ming Zhang Olivia Y. Xiao Johan Lim Xinlei Wang |
| author_facet | Ming Zhang Olivia Y. Xiao Johan Lim Xinlei Wang |
| author_sort | Ming Zhang |
| collection | DOAJ |
| description | Abstract Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were proposed to assess the fit of RE model. However, they tend to perform poorly when assessing rare binary events. Under a general binomial-normal framework, we propose a novel GOF test for the meta-analysis of rare events. Our method is based on pivotal quantities that play an important role in Bayesian model assessment. It further adopts the Cauchy combination idea proposed in a 2019 JASA paper, to combine dependent p-values computed using posterior samples from Markov Chain Monte Carlo. The advantages of our method include clear conception and interpretation, incorporation of all data including double zeros without the need for artificial correction, well-controlled Type I error, and generally improved ability in detecting model misfits compared to previous GOF methods. We illustrate the proposed method via simulation and three real data applications. |
| first_indexed | 2024-03-09T15:15:38Z |
| format | Article |
| id | doaj.art-7a8c44a0fb49487990fc18f5e2bc9661 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:15:38Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7a8c44a0fb49487990fc18f5e2bc96612023-11-26T13:05:58ZengNature PortfolioScientific Reports2045-23222023-10-0113111610.1038/s41598-023-44638-xGoodness-of-fit testing for meta-analysis of rare binary eventsMing Zhang0Olivia Y. Xiao1Johan Lim2Xinlei Wang3Department of Statistics and Data Science, Southern Methodist UniversityHighland Park High SchoolDepartment of Statistics, Seoul National UniversityDepartment of Statistics and Data Science, Southern Methodist UniversityAbstract Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were proposed to assess the fit of RE model. However, they tend to perform poorly when assessing rare binary events. Under a general binomial-normal framework, we propose a novel GOF test for the meta-analysis of rare events. Our method is based on pivotal quantities that play an important role in Bayesian model assessment. It further adopts the Cauchy combination idea proposed in a 2019 JASA paper, to combine dependent p-values computed using posterior samples from Markov Chain Monte Carlo. The advantages of our method include clear conception and interpretation, incorporation of all data including double zeros without the need for artificial correction, well-controlled Type I error, and generally improved ability in detecting model misfits compared to previous GOF methods. We illustrate the proposed method via simulation and three real data applications.https://doi.org/10.1038/s41598-023-44638-x |
| spellingShingle | Ming Zhang Olivia Y. Xiao Johan Lim Xinlei Wang Goodness-of-fit testing for meta-analysis of rare binary events Scientific Reports |
| title | Goodness-of-fit testing for meta-analysis of rare binary events |
| title_full | Goodness-of-fit testing for meta-analysis of rare binary events |
| title_fullStr | Goodness-of-fit testing for meta-analysis of rare binary events |
| title_full_unstemmed | Goodness-of-fit testing for meta-analysis of rare binary events |
| title_short | Goodness-of-fit testing for meta-analysis of rare binary events |
| title_sort | goodness of fit testing for meta analysis of rare binary events |
| url | https://doi.org/10.1038/s41598-023-44638-x |
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