在分析具有连续结果的随机试验中处理错误分类的分层变量
Lisa N Yelland1,2, Jennie Louise3, Brennan C Kahan4
1Women and Kids Theme, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
Statistics in medicine
|June 27, 2023
概括
在临床试验中调整分层变量是复杂的,当错误发生时. 模拟显示使用"更新的层"是建议准确分析当错误分类是存在的.
科学领域:
- 临床试验方法论 临床试验方法论
- 生物统计学 生物统计学
- 统计分析 统计分析
背景情况:
- 分层随机化在临床试验中常见,以平衡基线共变量.
- 错误分类的分层变量使分析复杂化,导致参与者被随机分配到错误的分层.
- 对错误分类的分层变量进行适当的分析调整仍然不清楚.
研究的目的:
- 在连续结果分析中,对错误分类的分层变量进行调整的不同方法进行比较.
- 在不同的错误发现场景下评估方法,并对治疗或相互作用效应感兴趣.
主要方法:
- 进行了模拟研究,以比较分析调整方法.
- 方法包括没有调整,随机化层调整,真层和更新层.
- 线性回归模型用于数据分析.
主要成果:
- 未调整的模型在所有模拟设置中表现不佳.
- 对真实层次的调整产生了最佳的结果.
- 调整随机化或更新分层的性能因设置而异.
结论:
- 当真实层次未知时,建议根据更新的层次进行调整和执行子组分析,假设错误发现独立于治疗组.
- 在报告分层错误和分析中处理这些错误时,提高透明度对于可靠的试验结果至关重要.
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