固定效应模型,用于对小数量集群和连续结果的阶段集群试验进行可靠的分析:一个模拟研究
Kenneth Menglin Lee1, Yin Bun Cheung2,3,4
1Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore. klee@u.duke.nus.edu.
Trials
|October 25, 2024
概括
固定效应模型提供了更好的I型错误控制和功率,用于步骤式集群试验 (SW-CTs),只有少数集群和连续结果. 这种强大的方法自动调整共变异不平衡,与混合效应模型不同.
科学领域:
- 临床试验方法论 临床试验方法论
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 逐步集群试验 (SW-CTs) 越来越多地使用,但可能会遭受共同变量失衡,特别是只有少数集群.
- 现有的方法,如共变量受约束的随机化和混合效应模型调整,需要已知的共变量.
- 固定效应模型提供了一个替代方案,通过自动调整时间不变的协变量,测量和非测量.
研究的目的:
- 在SW-CT中比较固定效应和混合效应模型的性能.
- 评估随机和非随机SW-CTs中的模型性能,具有连续结果和小集群数量.
- 在集群级别的共变异不平衡和混杂下评估模型的稳定性.
主要方法:
- 进行了模拟研究,以比较固定效应和混合效应模型.
- 场景包括随机和非随机的SW-CT,具有连续的结果和小集群数量.
- 模拟包括集群级共变异不平衡和混.
主要成果:
- 混合效应模型显示覆盖率低,I型错误率膨胀,结果连续,特别是只有少数集群或低ICC.
- 混合效应模型中的小样本校正不能完全解决I型错误问题.
- 固定效应模型始终实现了目标覆盖率和I型错误率,在最小的功率妥协下,并稳定地处理了协变异不平衡.
结论:
- 固定效应模型被推用于分析少数集群和连续结果的SW-CTs.
- 它能够控制I型错误并自动调整共变量的能力使其成为一个强大的选择.
- 这种方法提高了面临共同变量失衡的SW-CT估计的可靠性.
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