通过使用不同的统计方法,改进心血管试验中认知结果的分析
Shun Fu Lee1,2, William Whiteley3,4, Jackie Bosch5,6
1Population Health Research Institute, McMaster University, Hamilton, ON, Canada. shunfu.lee@phri.ca.
Trials
|October 2, 2024
概括
贝塔双项回归为分析认知得分提供了卓越的模型性能,提高了准确性并避免了临床试验中的误导性结果. 这种方法增强了对认知指标的直接解释.
科学领域:
- 临床试验 临床试验
- 生物统计学 生物统计学
- 认知神经科学 认知神经科学
背景情况:
- 迷你心理状态检查 (MMSE) 和蒙特利尔认知评估 (MoCA) 是测量临床试验中的全球认知标准工具.
- 分析离散和有限的认知得分作为连续的结果,由于非正常性和天花板/地板效应,这给统计学带来了挑战.
- 线性回归模型中违反的正常性假设可能导致无法检测出有意义的关联.
研究的目的:
- 实证地比较各种统计方法的模型性能来分析认知评估得分.
- 评估多变量线性回归 (MLR) 与转换,β-双项回归和托比特回归.
- 通过使用具有可比度单位的方法来评估治疗效应.
主要方法:
- 利用了来自四个大型随机对照试验的数据:起源,超越,指南针和导航-ESUS.
- 员工使用Akaike信息标准 (AIC) 来评估模型的性能.
- 在未转换的MLR,β-双项回归和托比特回归之间比较治疗效应.
主要成果:
- 在所有研究中,β-双项回归始终显示出优越的模型性能,这是所有研究中最低的AIC值所表明的.
- 使用平方根和日志转换的MLR也表现出良好的表现.
- 在ORIGIN试验中,β-双项回归发现了显著的治疗效果,与未转换的MLR和托比特回归不同.
结论:
- 建议使用β-双项回归来分析离散和有限的认知结果作为连续变量.
- 这种方法提高了模型的性能,防止了虚假的意义,并允许直接解释认知措施.
- 统计建模选择显著影响在认知试验中检测治疗效应.
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