在对二进制结果的元分析中,在具有恒定权重的Q统计上
Elena Kulinskaya1, David C Hoaglin2
1School of Computing Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK. e.kulinskaya@uea.ac.uk.
BMC medical research methodology
|June 21, 2023
概括
这项研究引入了用于元分析异质性测试的新Q统计,使用样本大小而不是估计的差异. 推的近似值提高了对日志概率,日志相对风险和风险差异指标的准确性.
科学领域:
- 生物统计学 生物统计学
- 医学研究方法学 医学研究方法学
- 统计建模 统计建模
背景情况:
- 科克兰的Q统计是元分析异质性测试的标准.
- 目前的方法通常使用估计的重量差异,使分布近似变得复杂.
- 这种复杂性影响了研究间差异估计器的准确性.
研究的目的:
- 根据样本大小,使用常量权重来研究一个替代的Q统计.
- 评估这个新的Q统计和相关措施分布的近似值.
- 为元分析中的二元效应测量提供改进的异质性测试方法.
主要方法:
- 开发了一个Q统计 ([公式:参见文本]) 使用仅研究样本大小的恒定权重.
- 进行模拟以研究日志概率 (LOR),日志相对风险 (LRR) 和风险差异 (RD) 的分布近似值.
- 马近似,Farebrother算法和标准千平方近似的性能比较.
主要成果:
- 对于LOR和LRR,对于小样本大小,双瞬时马近似是有效的;对于较大的样本大小,建议使用Farebrother算法.
- 对于 RD,Farebrother的近似在不同样本大小中表现良好.
- 标准的千二次近似对于所有三种二进制效应措施都是不够的.
结论:
- 对于Q的标准千二次近似,对于二进制效应测量来说是不可靠的.
- 基于新的Q统计数据,推异质性测试的替代近似方法.
- 提供了在0.05显著性水平选择适当测试的实践指南.
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