修改Poisson回归的合适性测试可能产生超过1的合适值,在二进制结果分析中
Yasuhiro Hagiwara1, Yutaka Matsuyama1
1Department of Biostatistics, School of Public Health, The University of Tokyo, Japan.
Statistical methods in medical research
|May 23, 2024
概括
本研究引入了修改Poisson回归的新适合性测试,这是一种对二进制结果的方法. 建议使用规范化余平方和试验,因为它在评估模型适配时具有可靠的性能.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 统计建模 统计建模
背景情况:
- 修改Poisson回归对于估计二元结果分析中的风险和流行率是有价值的.
- 现有的适合性测试仅限于修改Poisson回归,阻碍了模型验证.
- 修改Poisson回归中的不受约束的参数空间可能导致拟合值超过1,使标准测试应用复杂化.
研究的目的:
- 提出和评估专门设计用于修改波桑回归的新型适合性测试.
- 确定适当的统计测试,以评估修改后的Poisson模型的合适性.
- 在修改Poisson回归的背景下,解决现有的适合性测试的局限性.
主要方法:
- 开发了几种适合性测试:经过修改的Hosmer-Lemeshow与实证变量,Tsiatis测试,规范化的Pearson千平方测试 (二项式和Poisson变量) 和规范化的平方余和测试.
- 模拟研究,以评估关于I型错误和功率的拟议测试的性能.
- 将测试应用于癌症患者的横截面数据.
主要成果:
- 原来的Hosmer-Lemeshow和规范化的Pearson二项变量chi-square测试不适合修改Poisson回归.
- 正常化的平方余和测试在模拟中显示出强大的性能,特别是在I型错误控制和对错误链接函数的功率方面.
- 拟议的测试成功地应用于现实世界的横截面癌症数据.
结论:
- 正常化余平方和试验是修改Poisson回归的可靠和推的适合性测试.
- 该研究为在流行病学和生物统计学研究中验证修改Poisson模型提供了必要的工具.
- 准确的模型匹配评估对于可靠估计风险和流行率的估计至关重要.
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