如何通过基于模型的方法在阶段形试验中实现模型强大的推断?
Bingkai Wang1, Xueqi Wang2,3, Fan Li2,4
1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States.
Biometrics
|November 5, 2024
概括
基于模型的阶段形设计分析可以提供一致的治疗效应估计,即使使用错误指定的工作模型. 正确指定治疗效果结构是步骤集群随机试验中准确结果的关键.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 临床试验 临床试验
背景情况:
- 渐进形设计越来越多地用于集群随机试验.
- 基于模型的分析是评估这些设计中的治疗效应的标准.
- 在模型错误规范下进行这些分析的属性尚未得到充分理解.
研究的目的:
- 调查基于模型的阶段形设计方法为边际处理效应提供一致估计的条件.
- 确定工作模型错误规范对这些分析有效性的影响.
- 为了确定可靠推断的要求.
主要方法:
- 专注于线性混合模型和各种工作相关性结构的概括估计方程.
- 对非参数边际治疗效应估计的一致性的理论分析.
- 使用三明治差异估计器和g计算来进行可靠的推断.
主要成果:
- 非参数估计值的一致性通常需要正确指定的治疗效应结构.
- 工作模型的其他方面 (共变量,随机效应,错误分布) 可能被错误指定.
- 三明治差异估计器提供了有效的推理;对于非身份链函数或比率估计,需要g计算.
结论:
- 基于模型的阶梯形设计的分析可以对某些类型的模型错误规格进行强大的分析.
- 对治疗效果的正确规范对于有效估计至关重要.
- 这些发现为分析阶段形试验和确保可靠的治疗效果估计提供了指导.
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