Assessing Laboratory Method Validations for Informing Inference Across Survey Cycles in the National Health and Nutrition Examination Survey.

Kevin Chuang, Jennifer Rammon, Hee-Choon Shin, Te-Ching Chen
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Abstract

Background and objectives Laboratory tests conducted on survey respondents' biological specimens are a major component of the National Health and Nutrition Examination Survey. The National Center for Health Statistics' Division of Health and Nutrition Examination Surveys performs internal analytic method validation studies whenever laboratories undergo instrumental or methodological changes, or when contract laboratories change. These studies assess agreement between methods to evaluate how methodological changes could affect data inference or compromise consistency of measurements across survey cycles. When systematic differences between methods are observed, adjustment equations are released with the data documentation for analysts planning to combine survey cycles or conduct a trend analysis. Adjustment equations help ensure that observed differences from methodological changes are not misinterpreted as population changes. This report assesses the reliability of statistical methods used by the Division of Health and Nutrition Examination Surveys when conducting method validation studies to address concerns that adjustment equations are being overproduced (recommended too frequently). Methods Public-use 2017-2018 National Health and Nutrition Examination Survey laboratory data were used to simulate "new" measurements for 120 analytic method validation studies. Blinded studies were analyzed to determine the final adjustment recommendation for each study using difference plots, descriptive statistics, t-tests, and Deming regressions. Final recommendations were compared with simulated difference types to assess how often spurious results were observed. Concordance estimates (concordance, misclassification, sensitivity, specificity, and positive and negative predictive values) informed assessments. Results Adjustment equations were appropriately recommended for 75.0% of the studies, over-recommended for 5.8%, under-recommended for 15.8%, and recommended with an inappropriate technique for 3.3%. Across simulated difference types, sensitivity ranged from 65.9% to 84.4% and specificity from 74.7% to 97.5%. Conclusions Findings from this report suggest that the current methodology used by the Division of Health and Nutrition Examination Surveys performs moderately well. Based on these data and analyses, underadjustment was more prevalent than overadjustment, suggesting that the current methodology is conservative.

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评估实验室方法验证,为全国健康与营养调查中跨调查周期的推论提供依据。
背景和目标 对调查对象的生物标本进行实验室检测是全国健康与营养状况调查的主要组成部分。每当实验室在仪器或方法上发生变化,或者合同实验室发生变化时,国家卫生统计中心的健康与营养检查调查处都会进行内部分析方法验证研究。这些研究对各种方法之间的一致性进行评估,以评价方法的变化会如何影响数据推断或损害各调查周期测量的一致性。当观察到方法之间存在系统性差异时,将随数据文档发布调整方程,供计划合并调查周期或进行趋势分析的分析人员使用。调整方程有助于确保观察到的方法变化差异不会被误解为人口变化。本报告评估了健康与营养检查调查司在进行方法验证研究时所使用的统计方法的可靠性,以解决人们对调整方程制作过多(推荐频率过高)的担忧。方法 在 120 项分析方法验证研究中,使用 2017-2018 年国家健康与营养检查调查实验室公共使用数据来模拟 "新 "测量。使用差异图、描述性统计、t 检验和戴明回归对盲法研究进行分析,以确定每项研究的最终调整建议。将最终建议与模拟差异类型进行比较,以评估观察到虚假结果的频率。一致性估计值(一致性、误分类、灵敏度、特异性以及阳性和阴性预测值)为评估提供了依据。结果 75.0%的研究推荐了适当的调整方程,5.8%的研究推荐过度,15.8%的研究推荐不足,3.3%的研究推荐了不适当的技术。在所有模拟差异类型中,灵敏度从 65.9% 到 84.4% 不等,特异性从 74.7% 到 97.5%。结论 本报告的研究结果表明,健康与营养状况调查部目前使用的方法效果一般。根据这些数据和分析,调整不足比调整过度更为普遍,这表明目前的方法是保守的。
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来源期刊
CiteScore
2.50
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0.00%
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期刊介绍: Reports describing the general programs of the National Center for Health Statistics and its offices and divisions and the data collection methods used. Series 1 reports also include definitions and other material necessary for understanding the data.
期刊最新文献
Plan and Operations of the National Health and Nutrition Examination Survey, August 2021-August 2023. Assessing Laboratory Method Validations for Informing Inference Across Survey Cycles in the National Health and Nutrition Examination Survey. Developing Sampling Weights for Statistical Analysis of Parent-Child Pair Data From the National Health Interview Survey. Validation of the Enhanced Opioid Identification and Co-occurring Disorders Algorithms. National Center for Health Statistics' 2019 Research and Development Survey, RANDS 3.
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