基于Web的工具用于定量偏差分析的应用:由于自我报告的身体质量指数导致错误分类的例子
Hailey R Banack1, Samantha N Smith2, Lisa M Bodnar3
1From the Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Epidemiology (Cambridge, Mass.)
|February 1, 2024
概括
Apisensr对健康研究中的偏见进行了定量调整,揭示了自我报告的肥胖低估了糖尿病风险. 这种工具有助于研究人员纠正错误分类偏差,以获得更准确的发现.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 在观察性研究中,定量偏差分析对于解决错误分类,选择偏差和未测量的混至关重要.
- 自我报告的体重指数 (BMI) 经常用于定义肥胖,但其准确性可能导致暴露错误分类偏差.
- 肥胖和糖尿病之间的关系是偏见分析可以改进理解的关键领域.
研究的目的:
- 介绍Apisensr,这是一个基于Web的应用程序,用于实施定量偏差分析.
- 通过使用自我报告的BMI评估肥胖-糖尿病关系中的暴露错误分类偏差来证明Apisensr的实用性.
- 为其他研究人员可以利用的自我报告肥胖症提供偏差参数估计.
主要方法:
- 利用了来自国家健康和营养检查调查的公开数据.
- 估计偏差参数 (灵敏度,特异性,PPV,NPV) 对于自我报告的肥胖与人口群体间测量的BMI.
- 应用Apisensr调整肥胖和糖尿病分析中的暴露错误分类.
主要成果:
- 在自我报告和测量肥胖之间观察到显著的差异,因性别,年龄和种族种族而异 (敏感度:75%-89%;特异性:91%-99%).
- 使用Apisensr的定量偏差分析始终表明,自我报告的肥胖症低估了所有人口结构层与糖尿病的关联.
- 例如,在40-59岁的非西班牙裔白人男性中,糖尿病的几率比率从3.06 (自我报告) 增加到4.11 (因错误分类而调整).
结论:
- Apisensr是一个用户友好的,基于Web的Shiny应用程序,简化了定量偏差分析.
- 该研究为自我报告的肥胖提供了有价值的偏差参数估计,有助于未来对肥胖相关健康结果的研究.
- 准确的偏差调整对于可靠的流行病学发现至关重要,特别是在使用自我报告的健康措施时.
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