网络元分析对于一个顺序结果,当结果分类在试验之间有所不同时
Paul Morris1, Chong Wang2,3, Annette O'Connor4,5
1Department of Statistics, Iowa State University, Ames, 50010, IA, USA.
Systematic reviews
|May 9, 2024
概括
一种新的网络元分析方法处理不同试验的不同顺序结果分类. 这种方法通过使用所有可用的数据来改善估计,即使每个类别的试验有限.
科学领域:
- 生物统计学 生物统计学
- 证据综合 证据综合
- 相对有效性研究研究比较
背景情况:
- 随机对照试验经常使用二进制结果,但顺序结果 (例如疾病严重程度) 也很常见.
- 在试验中对顺序结果的分类变化给标准网络元分析带来了挑战.
- 研究合成器需要方法来解决网络元分析中不一致的结果分类.
研究的目的:
- 提出一个新的网络元分析模型,用于顺序结果,以适应试验中的不同分类.
- 允许在网络元分析中使用所有可用的数据,即使在不同研究中结果水平的组合不同.
主要方法:
- 开发了一个网络元分析模型,用于顺序结果,允许多个分类.
- 修改的多项概率将来自试验的部分信息纳入组合水平的试验.
- 采用贝叶斯固定效应模型与相邻类别的逻辑链接来解释平凡性.
主要成果:
- 拟议的方法是使用实践中的抗生素试验网络来说明用于预防牛肝的方法.
- 模拟显示了相对较小的偏差,即使在试验中的分类不同.
- 较大的样本大小导致了较小的平均平方根误差,表明了良好的估计特性.
结论:
- 拟议的相邻类别逻辑链接方法在网络元分析中表现良好,具有不同的顺序结果分类.
- 这种方法对于研究合成器来说特别有价值,这些合成器处理的网络包含针对特定结果分类的有限试验.
- 通过将所有数据考虑在一个单一的估计中,该方法提高了证据综合的效率和可靠性.
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