在没有报告的危险比率的小型临床试验中估计危险比率
Shuyuan Wang1, Ziwei Song2, Mingjie Wu3
1Department of Cardiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
Discover oncology
|May 24, 2025
概括
研究人员开发了一种简单的方法,用卡普兰-梅尔曲线来估计生存研究中的危险比率 (HRs). 这种方法可以将更多的研究纳入元分析,提高临床证据的可靠性.
科学领域:
- 生物统计学 生物统计学
- 临床试验 临床试验
- 生存分析的分析.
背景情况:
- 对危险比率 (HRs) 的元分析对于生存研究中可靠的结论至关重要.
- 一个显著的限制是临床试验中经常没有报告的HRs.
研究的目的:
- 开发一种简单的方法来从卡普兰-梅尔 (KM) 曲线中估计HRs.
- 为了使没有报告HR的研究能够被纳入元分析中.
主要方法:
- 通过从公布的KM曲线中提取时间点和生存率来重建生命表.
- 使用反向导出来确定生命表参数.
- 通过SPSS软件使用考克斯回归重新绘制KM曲线和估计HRs.
主要成果:
- 该方法在三个例子中得出了与原始值可比的估计HRs.
- 估计HRs:0.510 (CI 0.272-0.958),2.472 (CI 1.548-3.949),0.591 (CI 0.291-1.199). 在此之前,我们已经发现了一些问题.
- 原始HRs:0.51 (CI 0.27-0.96),2.33 (CI 1.45-3.73),0.62 (CI 0.31-1.26). 在此之前,这些HRs是指原始HRs.
结论:
- 这种简单的方法在没有报告HR的生存研究中有效地从KM曲线中估计HR.
- 便于将更多的研究纳入元分析,从而增加样本大小.
- 提高综合临床证据的整体可靠性和稳定性.
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