对时间到事件终点的缺失数据的归算,使用检索的丢失值.
Shuai Wang1, Robert Frederich2, James P Mancuso3
1Pfizer Inc., 1 Portland St, Cambridge, MA, 02139, USA. shuai1107@hotmail.com.
Therapeutic innovation & regulatory science
|October 7, 2023
概括
这项研究引入了一种新的方法,用于在临床试验中使用检索的失败数据来归因失踪的时间到事件数据. 这种方法为传统方法提供了强大的替代方案,提高了试验结果的可靠性.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 生存分析的分析.
背景情况:
- 缺少时间到事件数据是长期结果试验中常见的挑战.
- 标准归算方法通常依赖于诸如随机审查 (CAR) 等假设,这些假设可能并不总是成立.
- 需要强大的统计方法来处理缺少的数据并确保可靠的试验结果.
研究的目的:
- 提出和评估一种新的统计方法,用于使用检索的丢失数据来归纳缺失的时间到事件数据.
- 将这种新方法的性能与现有的参数和非参数归算技术进行比较.
- 评估不同归算策略的稳定性和计算效率.
主要方法:
- 提出了一个多重归算框架,使用检索掉落来建模丢失的时间到事件数据.
- 对比参数 (MCMC,MLE) 和非参数 (bootstrap) 归算方法.
- 在各种场景中评估了I型错误和功率率.
- 应用于CVOT第三阶段数据集的方法,与考克斯模型和跳转引用归算进行比较.
主要成果:
- 拟议的检索失学归算方法在模拟中显示了可比或优越的性能.
- 参数和非参数方法在计算强度和分布假设方面表现出不同的优势.
- 对CVOT数据集的应用说明了拟议方法的实际实用性和稳定性.
结论:
- 在临床试验中,检索到的脱落数据提供了一个合理的依据,用于归因临床试验中缺失的时间到事件数据.
- 拟议的多重归算方法为标准方法提供了有价值的替代方案,特别是当CAR假设存在疑问时.
- 需要对各种试验环境中的性能特征进行进一步的研究.
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