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计算风险和患病率以及R的差异:通过实践教程和代码开发直觉
Rachel R Yorlets1, Youjin Lee2, Jason R Gantenberg3
1Department of Epidemiology, Brown University School of Public Health, Providence, RI; Population Studies and Training Center, Brown University, Providence, RI.
Annals of epidemiology
|August 12, 2023
概括
本教程指导研究人员为二进制结果选择合适的统计措施,超越使用R代码的几率比率与风险或流行率比率. 它增强了在流行病学研究中对这些方法的理解和应用.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生研究 公共卫生研究
背景情况:
- 流行病学研究经常涉及二元结果,但对于选择适当的关联措施的明确指导往往缺乏.
- 错误地应用统计措施,例如仅依赖几率比率,可能导致二进制结果研究中的误导性结论.
- 现有的课程和文献可能不充分涵盖估计风险或患病率的方法.
研究的目的:
- 为流行病学研究中的二元结果选择和计算适当的关联度提供实用教程.
- 通过使用注释的R代码来证明四种方法的应用和比较,以估计风险或流行率 (或差异).
- 引导研究人员了解何时使用特定方法,它们的优点,局限性和解释.
主要方法:
- 该研究提供了一个使用R统计编程语言的实践教程.
- 提供注释代码以说明计算和比较四种不同的方法来估计风险或流行率.
- 提供了关于每个方法适当的用例,优势和局限性的指导.
主要成果:
- 该教程使读者能够应用,比较和理解四种方法来估计风险或流行率,并将它们与赔率比对比.
- 读者可以通过注释的R代码获得实践经验,从而促进他们在研究中直接实施.
- 该研究有助于对不同估计方法获得的结果进行比较理解.
结论:
- 该资源使学员,公共卫生研究人员和跨学科专业人员能够自信地实施和解释风险或流行率比率.
- 通过超越几率比率,研究人员可以获得更准确和可解释的二进制结果的统计结论.
- 该教程促进了在流行病学研究中选择和应用适当的统计措施的更深层次的直觉.
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