多重归算的全面实施,使用用于连续终点的检索失效的检索失效.
Shuai Wang1, Pamela F Schwartz2, James P Mancuso2
1Pfizer Research & Development, Pfizer Inc, New York, NY, USA. shuai1107@hotmail.com.
BMC medical research methodology
|February 21, 2025
概括
一种新的基于检索失业者 (MIRD) 的多重归算方法使用所有可用的失业者数据提供了与已建立的在代谢疾病中纵向数据分析方法相比较的性能. 这种方法,特别是单步MCMC,在某些场景中显示出更好的功率和I型错误控制,简化了临床试验报告.
科学领域:
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
- 代谢性疾病研究研究
背景情况:
- 重复测量混合模型 (MMRM) 面临的挑战是,在代谢性疾病中缺少数据.
- 基于检索掉落的多重推算 (MIRD) 正在成为纵向终点的标准.
- 现有的MIRD方法通常依赖于最后一次处理数据进行归算.
研究的目的:
- 引入和评估一类新的MIRD方法,利用所有可用的数据从检索掉队者 (RDs).
- 为了比较新的MIRD方法与既有MIRD和其他统计方法 (如MMRM) 的性能.
- 在各种缺失数据场景下评估I型错误和功率率.
主要方法:
- 建议使用所有可用的 RD 数据进行新的 MIRD 方法,通过一步式 MCMC 或两步式回归实现.
- 应用ANCOVA后计算和Rubin结合估计的规则.
- 进行了广泛的模拟研究,并分析了两个现实世界第三期临床试验数据集.
主要成果:
- 新的MIRD类表现出与已建立的MIRD方法可比的性能.
- 一步式MCMC MIRD方法在特定场景中显示出更好的I型错误控制和更大的功率.
- 现实世界的数据分析证实了新的MIRD类的增强能力,特别是在更大的数据集中.
结论:
- 建议使用所有观察到的RD数据的MIRD方法是纵向连续终点的强大和强大的替代方案.
- 一步式的MCMC实施在统计能力和I型错误控制方面提供了优势.
- 由于编程更简单,预计这种新的MIRD类可以更容易地在临床试验报告中实施.
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