使用先前的纵向组随机化农村减肥研究数据来设计一个未来的农村减肥试验
Alexandra R Brown1,2, Byron J Gajewski1,2, Matthew S Mayo1,2
1Department of Biostatistics & Data Science, The University of Kansas Medical Center, Kansas City, KS, USA.
一个新的随机组试验纵向模型显示了可比的功率和可接受的I型错误率,验证了它在未来研究中的使用. 这种统计分析确保了复杂的层次数据的可靠结果.
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
背景情况:
- 群组随机试验 (GRT) 由于层次数据结构而存在独特的挑战.
- 纵向GRT通过嵌套的数据层增加了进一步的复杂性.
- 现有的模拟研究通常依赖于参数假设,限制了概括性.
研究的目的:
- 将拟议的纵向混合效应模型的性能与GRT标准基线调整模型进行比较.
- 用数据驱动模拟来评估纵向模型的I型错误率和实证功率.
- 为了验证纵向模型适用于前性研究的适用性,分析%重量变化.
主要方法:
- 使用现有研究数据生成数据驱动的模拟,以告知模型假设.
- 一个带有三个随访时间点的纵向混合效应模型与基线调整模型进行了比较.
- 实证功率和I型错误率被计算为连续结果 (24个月的%重量变化) 跨越不同的效果大小.
主要成果:
- 两种模型在测试的效果大小中都显示出可比的经验功率.
- 纵向模型显示I型错误率为3.09%,而基线调整后的模型显示3.87%.
结论:
- 建议的纵向混合效应模型不会膨胀I型错误率.
- 经过验证的纵向模型适用于未来的群体随机试验,使用层次和纵向数据.
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