可以使用预测间隔对GRADE的不精确性和不一致性领域进行同时评估
M Hassan Murad1, Rebecca L Morgan2, Yngve Falck-Ytter2
1Evidence-based Practice Center, Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA; Evidence Foundation, Cleveland Heights, OH, USA.
Journal of clinical epidemiology
|September 25, 2024
概括
预测间隔 (PI) 提供了一种可行的方法,可以同时评估元分析中的不准确性和不一致性,这可能导致确定性评级降低. 建议在系统性审查中对这种新方法进行进一步的测试.
科学领域:
- 医学统计 医学统计
- 证据综合 证据综合
背景情况:
- 建议评级,评估和评估 (GRADE) 框架评估证据的确定性.
- 不准确性和不一致性是GRADE的关键领域,传统上被单独评估.
- 利益相关者定义的门对于证据评估中的决策至关重要.
研究的目的:
- 评估预测间隔 (PI) 的实用性,同时评估GRADE的不准确性和不一致性领域.
- 将基于PI的新方法与使用利益相关方提供的决策值的传统方法进行比较.
主要方法:
- 转换的元分析PI从相对风险到绝对风险差异使用基线风险.
- 将转换的PI与利益相关方提供的绝对规模值进行了比较.
- 将该方法应用于来自Cochrane系统审查数据库的2516个元分析.
- 与PI方法对比,对不准确性 (信心区间) 和不一致性 (异质性措施) 的单独评估.
主要成果:
- 在59%的元分析中,PI方法产生了与传统方法相同的确定性评级.
- 在27%的案例中,PI方法导致了较低的确定性评级,而在14%的案例中,则导致了更高的确定性评级.
- 敏感性分析证实了类似的趋势,PI宽度随着更多的研究和更高的I2值而增加.
结论:
- 使用PI来同时评估不准确性和不一致性是可行的和合乎逻辑的.
- 基于PI的方法可能会导致证据确定性的降级更频繁.
- 在系统审查和指导方针中进一步验证PI方法是合理的,使用特定的上下文值.
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