适合性测试用于对罕见二进制事件的元分析
Ming Zhang1, Olivia Y Xiao2, Johan Lim3
1Department of Statistics and Data Science, Southern Methodist University, Dallas, Texas, 75205, USA.
Scientific reports
|October 18, 2023
概括
一个新的适合性测试改进了对罕见事件的随机效应元分析. 这种方法通过控制错误并比现有方法更好地检测不合适来增强模型评估.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 统计建模 统计建模
背景情况:
- 随机效应 (RE) 的元分析对于合成异质研究结果至关重要.
- 现有的频率主义的合适性测试 (GOF) 在RE模型中与罕见的二进制事件作斗争.
- 准确的GOF评估对于可靠的元分析结论至关重要.
研究的目的:
- 开发一种新的适合性测试 (GOF),专门用于罕见事件的随机效应元分析.
- 解决目前GOF测试在处理稀疏数据和双零数据方面的局限性.
- 为了提供一个更可靠,更易于解释的GOF评估方法.
主要方法:
- 建议在罕见事件元分析的一般双项-正常框架下进行新的GOF测试.
- 利用贝叶斯模型评估中的关键量.
- 采用从马尔科夫链蒙特卡洛后面样本中得出的依赖p值的考奇组合.
主要成果:
- 新的GOF测试显示了控制良好的I型错误率.
- 与现有的频率主义方法相比,它在检测模型不匹配方面表现出更好的能力.
- 该方法有效地纳入了所有数据,包括双零,没有人工纠正.
结论:
- 开发的GOF测试为评估具有罕见二进制事件的随机效应元分析模型提供了卓越的方法.
- 它提供了更清晰的解释和强大的性能,提高了元分析结果的可靠性.
- 该方法通过模拟和现实世界的数据应用得到验证.
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