计算计划中的个人参与者数据元分析的功率,以检查对二进制结果的预后因素影响
Rebecca Whittle1,2, Joie Ensor1,2, Miriam Hattle1,2
1Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
Research synthesis methods
|July 24, 2024
概括
研究人员现在可以在数据收集之前估计计划的个人参与者数据元分析 (IPDMA) 的统计能力. 该方法使用汇总数据来预测IPDMA功率,为预测因素研究节省时间和资源.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医学研究方法学 医学研究方法学
背景情况:
- 个人参与者数据元分析 (IPDMA) 是资源密集型的.
- 不够的统计能力可能会限制IPDMA项目的结论.
- 在启动IPDMA数据收集之前,准确的功率估计至关重要.
研究的目的:
- 建议一种方法来估计计划中的IPDMA的统计能力.
- 为了使研究人员能够在收集个人参与者数据 (IPD) 之前评估IPDMA功率.
- 通过合成队列研究,研究对二元结果的预后因素影响.
主要方法:
- 使用聚合数据估计IPDMA功率的三步方法.
- 步骤1:从公布的汇总数据中估计费舍尔信息矩阵,以估计研究特定的差异.
- 步骤2:估计总结预后效应的预期差异.
- 步骤3:使用估计的方差和假定的真实效应大小计算IPDMA功率.
主要成果:
- 拟议的方法提供了一种方法来估计计划中的IPDMA的功率.
- 该方法可以扩展,以考虑共变量和研究间异质性.
- 为实际实施提供了说明性示例和Stata代码.
结论:
- 预先估计IPDMA功率对于高效的研究设计至关重要.
- 拟议的方法有助于就IPDMA可行性和资源分配做出明智的决定.
- 这种方法通过增强的元分析规划来支持强大的预后因素研究.
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