时间恒定的绝对效果衡量时间到事件的结果.
Oliver Kuss1,2,3, Annika Hoyer4
1Deutsches Diabetes-Zentrum, Institut für Biometrie und Epidemiologie, Auf'm Hennekamp 65, Düsseldorf, 40225, Germany. oliver.kuss@ddz.de.
BMC medical research methodology
|December 17, 2025
概括
新的参数添加性危险模型允许计算时间常数对时间到事件结果的绝对效应. 这种方法提供了一个单一的,可解释的需要治疗的数量 (NNT),以更好地沟通临床试验.
科学领域:
- 生物统计学 生物统计学
- 临床试验分析
- 生存分析的分析.
背景情况:
- 在临床试验中,报告相对和绝对治疗效应至关重要.
- 对于时间到事件数据来说,计算绝对指标,如需要处理的数量 (NNT),是具有挑战性的,因为时间依赖.
- 传统模型在处理结果分布和时间依赖方面存在局限性.
研究的目的:
- 提出和评估参数添加性危险模型,用于计算时间常数绝对效应测量在时间到事件结果.
- 为了在整个研究期间提供一个单一的,可解释的绝对效应大小 (例如,危险差异,NNT).
- 克服以前的方法在分配灵活性和时间依赖方面存在的局限性.
主要方法:
- 使用了一类参数增量危险模型来获取时间到事件数据.
- 配备了六种不同的参数分布 (指数式,线性危险率,韦布尔,逻辑逻辑,戈珀茨,马-戈珀茨).
- 将该方法应用于所有原因死亡率的EMPA-REG OUTCOME试验的数字化Kaplan-Meier数据.
主要成果:
- 尽管模型适合不同,但估计的速率差异和NNT在分布中是相似的.
- 最适合的模型 (线性危险率,Gompertz) 的率差为 -8.8/1000人年,NNT为114.
- 观察到风险增加,与所有原因的死亡率一致,估计的分布模式是合理的.
结论:
- 参数添加性危险模型提供了一种可靠的方法,用于计算时间常数绝对效应指标的时间到事件结果.
- 这种方法成功地解决了时间依赖和分布灵活性,产生可解释的绝对效应大小.
- 未来的研究可以探索更复杂的分布和时间尺度上的绝对量.
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