信息审查对随机试验中的估计和测试的影响,随着治疗效果的延迟
Jingyi Lin1, Yujie Zhao2, X Gregory Chen3
1Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States of America.
Contemporary clinical trials
|February 26, 2025
概括
瘤学试验中的信息审查可能会导致结果偏差,特别是在延迟治疗效果的情况下. 我们的研究使用形模型量化了这种偏差,揭示了对危险比率和统计能力的重大影响.
科学领域:
- 生物统计学 生物统计学
- 临床试验 临床试验
- 生存分析的分析.
背景情况:
- 瘤学试验中的不同审查模式可能表明信息审查,可能会偏见治疗效果估计.
- 信息审查的影响,特别是延迟治疗效果的影响,在现有的模拟研究中没有得到很好的描述.
研究的目的:
- 评估信息审查对治疗效果估计和瘤随机试验的统计能力的影响.
- 提出一种关于信息审查的强度的新方法,以及一种用于分析审查和事件时间相关性的视觉工具.
- 提高对依赖审查数据的copula模型的理解和应用.
主要方法:
- 使用copula方法来建模依赖审查数据并评估信息审查.
- 开发了一个新的衡量标准:事件被信息审查的概率.
- 提出了一个视觉工具来检查审查和事件时间之间的相关性.
- 在推迟治疗效果场景下进行了模拟研究,使用配方生存模型与片式指数边缘生成各种审查模式.
主要成果:
- 当审查时间和事件时间相关性在研究手臂之间的方向不同时,危险比率的显著高估和统计能力的损失发生了.
- 当相关性在跨臂的方向和大小相似时,对危险比率估计和统计能力的影响是最小的.
- 拟议的措施和视觉工具有助于理解信息审查的强度和模式.
结论:
- 信息审查对瘤学试验结果产生重大偏差风险,特别是影响危险比率估计和统计能力.
- 科普拉模型为分析依赖性审查和量化信息审查的影响提供了一个强大的框架.
- 仔细考虑审查模式和使用先进的统计方法对于可靠地解释瘤学试验结果至关重要.
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