在缺失共变量的共变量适应随机实验中的回归调整
Wanjia Fu1, Yingying Ma2, Hanzhong Liu1
1Department of Statistics and Data Science, Tsinghua University, Beijing, China.
Statistics in medicine
|November 7, 2025
概括
这项研究澄清了共变量适应随机化中缺少共变量数据的平均治疗效果估计器的统计特性. 它提供了有效推断的方法,增强了临床试验分析.
科学领域:
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
- 统计推理 统计推理
背景情况:
- 同变量适应性随机化平衡了临床试验中的预后因素.
- 回归调整可以提高估计效率.
- 缺少的共变量数据给分析带来了挑战.
研究的目的:
- 在共变量适应性随机化下,调查缺失共变量的平均治疗效果估计器的非对称性特性.
- 为有效的统计推理开发一致的差异估计器.
- 评估不同方法的有限样本性能.
主要方法:
- 将缺失数据处理程序与回归调整相结合.
- 对治疗效果估计器的非对称分析.
- 导出一致的差异估计器.
- 无模型分析,确保在错误规格下有效性.
主要成果:
- 对于平均治疗效果估计器的确定的非对称性质.
- 开发了可靠推断的一致差异估计器.
- 数字研究表明,在各种场景中表现得很好.
结论:
- 拟议的方法提供了一个强大的框架,用于分析在共变量适应随机化下缺少共变量数据的临床试验.
- 无模型方法确保有效性,即使在潜在的回归模型错误规范的情况下.
- 基于模拟结果,提供了实际应用的建议.
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