在使用治疗效果尺度时,先前的有效样本大小
Hongtao Zhang1, Keaven M Anderson1, Zachary Zimmer1
1Biostatistics and Research Decision Sciences, Merck & Co. Inc., North Wales, PA, USA.
Statistics in medicine
|August 22, 2025
概括
之前的有效样本大小 (ESS) 对贝叶斯式外部数据借用至关重要. 这项研究将预期的局部信息比率 (ELIR) ESS定义扩展到治疗效果尺度,解决了改善试验设计的关键方法差距.
科学领域:
- 生物统计学
- 临床试验
- 贝叶斯推理
背景情况:
- 在临床试验中越来越多地使用贝叶斯式外部数据借用.
- 精确的先前有效样本大小 (ESS) 对于控制借用信息至关重要.
- 现有的ESS方法主要集中在借款控制上,而不是治疗效果尺度.
研究的目的:
- 将预期的局部信息比率 (ELIR) 的ESS定义扩展到治疗效果尺度.
- 为各种终点和治疗效果测量提供一个一般框架并推导ESS.
- 评估拟议的ELIR ESS的预测一致性特性.
主要方法:
- 扩大预期的本地信息比率 (ELIR) 的ESS定义.
- 对各种终点类型和治疗效果的ESS导出.
- 对不同终点和治疗效果组合的预测一致性的评估.
主要成果:
- 已经成功地将ELIR ESS定义扩展到治疗效果尺度.
- 针对多个终点和治疗效果类型,先前的ESS公式得到了推导.
- 只有两个正常终点之间的差异保持了预测一致性.
结论:
- 开发的方法解决了在治疗效果尺度上计算先前的ESS的差距.
- 这些发现强调了在应用ELIR ESS时考虑终点和治疗效应类型的重要性.
- 已有R实施方案可用于在实践中促进这些新型ESS方法的应用.
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