随机临床试验中的时间到第一次事件和反复事件方法的比较
Brian Claggett1, Stuart Pocock2, L J Wei3
1Cardiovascular Division, Brigham and Women's Hospital, Boston, MA (B.C., M.A.P., S.D.S.).
Circulation
|March 29, 2018
概括
在慢性疾病试验中,反复发生的方法比时间到第一次的方法具有更大的统计能力,特别是在患者异质性较高和停药率较低的情况下. 这两种方法随着风险异质性的增加而失去效力.
科学领域:
- 临床试验方法
- 生物统计学
- 慢性疾病流行病学
背景情况:
- 第三阶段试验通常使用时间到第一次事件的终点.
- 为了更好地捕捉疾病负担,针对慢性疾病提出了重复事件方法.
- 假设这些方法比时间到第一个方法提高了统计精度和功率.
研究的目的:
- 描述影响反复事件和时间到第一次方法的统计性质的因素.
- 用模拟来评估慢性疾病的随机治疗.
- 将结果与实际随机临床试验进行比较.
主要方法:
- 模拟的1:1随机化对4000名患者进行主动治疗与对照治疗 (20%的风险降低).
- 患者在事件发生后的风险异质性和治疗中断.
- 评估反复事件和时间到第一个方法对统计功率和精度的影响.
主要成果:
- 增加的风险异质性降低了两种方法的功率;反复事件方法保持了真实的相对风险估计.
- 随着更高的断药率 (≥60%),复发事件方法的功率下降.
- 当停止治疗时,反复发生的方法比时间到第一次的方法更强大.
结论:
- 增加患者风险异质性降低了这两种方法的统计能力.
- 具有较高的患者异质性和较低的断药率的复发性方法提供了最大的统计收益.
- 实际临床试验数据支持模拟结果,特别是在慢性心力衰竭试验中.
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