在随机对照试验中分析患者报告的结果的统计方法的比较:一个模拟研究研究
Yirui Qian1,2, Stephen J Walters2, Richard M Jacques2
1Centre for Health Economics, University of York, York, UK.
Statistical methods in medical research
|October 23, 2024
概括
建议使用多重线性回归 (MLR) 来分析随机对照试验 (RCT) 中患者报告的结果 (PRO). 对于PRO数据,MLR显示出最小的偏差和适当的置信区间覆盖范围,PRO数据通常是离散的和倾斜的.
科学领域:
- 临床试验 临床试验
- 生物统计学 生物统计学
- 健康 结果 研究 研究 结果
背景情况:
- 患者报告结果 (PRO) 越来越多地被用于随机对照试验 (RCT),以捕捉主观的健康感知.
- PRO数据往往表现出诸如有界限,离散和倾斜等特征.
- 目前用于分析RCT中的PRO的统计方法缺乏关于最佳实践的最终共识.
研究的目的:
- 为了比较三个统计方法的性能:多重线性回归 (MLR),托比特回归 (Tobit) 和中位数回归 (中位数).
- 通过模拟方法在双臂均衡随机对照试验 (RCT) 设置中评估这些分析患者报告结果 (PRO) 的方法.
- 根据偏差,经验标准误差,信任区间覆盖范围,I型错误和统计能力来评估性能.
主要方法:
- 模拟研究比较MLR,托比特和中位数回归.
- 假设一个潜在的隐性连续结果,通过一个离散的,均距离的 PRO 测量.
- 在一个平衡的双臂RCT中,评估了不同场景的性能,并预先定义了治疗效果.
主要成果:
- 多重线性回归 (MLR) 在大多数场景中表现出最小的偏差,小的标准误差和适当的置信区间覆盖.
- 托比特回归显示,PRO的MLR表现低于PRO的MLR,只有少数离散水平,但随着更多水平的提高而有所改善.
- 中位数回归显示了大量的偏差和错误,导致功率和覆盖率低,特别是在少量离散值的情况下.
结论:
- 多重线性回归 (MLR) 被推作为一种简单且普遍适用的统计方法,用于分析随机对照试验 (RCT) 中患者报告的结果 (PRO).
- 方法的选择可能取决于PRO数据的特定特征,例如离散层次的数量.
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