在具有潜在分层的非线性回归模型中用于多参数评估的样本大小和功率确定.
Michael J Martens1,2, Soyoung Kim1,2, Kwang Woo Ahn1,2
1Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Biometrics
|June 26, 2023
概括
确定样本大小和生物医学研究的统计能力至关重要. 这项研究引入了一种简化,一般的方法来计算样本大小和功率,在回归模型中分析多个变量时,提高研究设计效率.
科学领域:
- 生物统计学 生物统计学
- 生物医学研究方法学
背景情况:
- 准确的样本大小和功率计算对于生物医学研究设计至关重要.
- 共变量调整和对多个变量 (例如治疗方法,风险因素) 的分析在回归建模中很常见.
- 现有的用多个预测因子和相关的共变量来确定样本大小/功率的方法通常是复杂的或依赖于耗时的模拟.
研究的目的:
- 为样本大小和功率的确定提出一种更简单,更普遍的方法.
- 为在各种回归模型中测试多个参数的研究提供准确的计算.
- 为了解决多个感兴趣的变量和共变量相关性产生的复杂性.
主要方法:
- 为样本大小和功率计算制定一个通用公式.
- 应用到一般化的线性模型,普通和分层的Cox和Fine-Gray模型.
- 通过严格的模拟和理论推导进行验证.
主要成果:
- 拟议的公式准确地确定样本大小,满足I型错误率和统计能力的研究规格.
- 在多个常用的回归模型中证明了准确性.
- 该方法简化了复杂的样本大小/功率计算.
结论:
- 开发的方法为复杂的生物医学研究中样本大小和功率的确定提供了更容易获得和更有效的方法.
- 这有助于在评估多个变量和共变量时进行可靠的研究设计.
- 这些发现支持改善规划和执行需要回归分析的生物医学研究.
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