Application of a Web-based Tool for Quantitative Bias Analysis: The Example of Misclassification Due to Self-reported Body Mass Index.

IF 4.7 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Epidemiology Pub Date : 2024-05-01 Epub Date: 2024-02-01 DOI:10.1097/EDE.0000000000001726
Hailey R Banack, Samantha N Smith, Lisa M Bodnar
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Abstract

Background: We describe the use of Apisensr, a web-based application that can be used to implement quantitative bias analysis for misclassification, selection bias, and unmeasured confounding. We apply Apisensr using an example of exposure misclassification bias due to use of self-reported body mass index (BMI) to define obesity status in an analysis of the relationship between obesity and diabetes.

Methods: We used publicly available data from the National Health and Nutrition Examination Survey. The analysis consisted of: (1) estimating bias parameter values (sensitivity, specificity, negative predictive value, and positive predictive value) for self-reported obesity by sex, age, and race-ethnicity compared to obesity defined by measured BMI, and (2) using Apisensr to adjust for exposure misclassification.

Results: The discrepancy between self-reported and measured obesity varied by demographic group (sensitivity range: 75%-89%; specificity range: 91%-99%). Using Apisensr for quantitative bias analysis, there was a clear pattern in the results: the relationship between obesity and diabetes was underestimated using self-report in all age, sex, and race-ethnicity categories compared to measured obesity. For example, in non-Hispanic White men aged 40-59 years, prevalence odds ratios for diabetes were 3.06 (95% confidence inerval = 1.78, 5.30) using self-reported BMI and 4.11 (95% confidence interval = 2.56, 6.75) after bias analysis adjusting for misclassification.

Conclusion: Apisensr is an easy-to-use, web-based Shiny app designed to facilitate quantitative bias analysis. Our results also provide estimates of bias parameter values that can be used by other researchers interested in examining obesity defined by self-reported BMI.

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应用网络工具进行定量偏差分析:以自报体重指数导致的误分类为例。
背景:我们介绍了 Apisensr 的使用方法,这是一种基于网络的应用程序,可用于对误分、选择偏倚和未测量混杂因素进行定量偏倚分析。在分析肥胖与糖尿病之间的关系时,我们以使用自我报告的体重指数(BMI)来定义肥胖状态导致的暴露误分类偏差为例,应用了 Apisensr:我们使用了美国国家健康与营养调查(NHANES)的公开数据。分析包括1)按性别、年龄和种族-人种估算自我报告肥胖的偏倚参数值(灵敏度、特异性、阴性预测值、阳性预测值),与按测量的体重指数定义的肥胖进行比较;2)使用 Apisensr 调整暴露误分类:结果:自我报告与测量肥胖之间的差异因人口群体而异(灵敏度范围:75% 至 89%;特异性范围:91% 至 99%)。使用 Apisensr 进行定量偏差分析,结果有一个明显的模式:在所有年龄、性别和种族人种类别中,使用自我报告与测量肥胖相比,肥胖与糖尿病之间的关系被低估了。例如,在 40-59 岁的非西班牙裔白人男性中,使用自我报告的体重指数得出的糖尿病患病几率比为 3.06(95% CI:1.78, 5.30),而经过偏差分析调整误分类后得出的患病几率比为 4.11(95% CI:2.56, 6.75):Apisensr是一款易于使用、基于网络的Shiny应用程序,旨在促进定量偏倚分析。我们的研究结果还提供了偏倚参数值的估计值,可供其他有兴趣研究由自我报告的体重指数定义的肥胖症的研究人员使用。
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来源期刊
Epidemiology
Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.70
自引率
3.70%
发文量
177
审稿时长
6-12 weeks
期刊介绍: Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.
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