对分层随机化的连续共变量进行分类:我们应该如何调整?
Thomas R Sullivan1,2, Tim P Morris3, Brennan C Kahan3
1Women and Kids Theme, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
Statistics in medicine
|March 15, 2024
概括
在使用类别的随机试验中为连续分层变量进行调整,可能会导致偏差的结果,特别是缺少数据. 建议使用像分数多项式这样的灵活方法来准确估计治疗效果.
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
背景情况:
- 在临床试验中使用分层随机化来确保预后因素的平衡.
- 连续变量经常被分类为分层,导致有关适当分析调整的问题.
研究的目的:
- 在分层随机化后评估不同的共同变量调整策略.
- 为了比较随机化类别与连续值的调整,考虑线性和非线性共变量-结果关系.
主要方法:
- 用数据模拟来评估对连续和二进制结果的调整策略.
- 分析包括未调整的模型,对类别进行调整,并使用线性模型调整连续值,分数多项式和受限制的立方线.
- 研究了完整与缺失的结局数据的影响.
主要成果:
- 未经调整的分析表现不佳.
- 调整方法错误地指定了共变量-结果关系,但效果不那么强大和偏差,特别是缺少数据.
- 随机化类别的调整往往导致最高程度的错误规范.
结论:
- 应避免对随机化类别进行调整.
- 对于连续分层变量,建议使用灵活的调整方法 (分数多项式,受限立方线),以防止错误规范和偏差.
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