Should biomarker estimates of HIV incidence be adjusted?
Should biomarker estimates of HIV incidence be adjusted?
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DOI：
10.1097/qad.0b013e3283269e28
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发表时间：
2009-02-20
期刊：
影响因子：
3.8
通讯作者：
Brookmeyer, Ron
中科院分区：
文献类型：
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作者：
Brookmeyer, Ron
目标：评估为纠正生物标志物横断面调查（例如 BED 捕获酶免疫测定）得出的 HIV 发病率而提出的调整程序。这些程序的动机是一些报道称，与队列研究相比，生物标志物 BED 方法高估了发病率。设计：考虑 Hargrove 和 McDougal 调整程序，调整 HIV 发病率的生物标志物估计值，以防止感染时间方面的错误分类。方法：对调整公式进行数学和统计分析。评估发病率队列研究中的错误来源，这也可以解释队列和生物标志物估计之间的差异。结果：麦克杜格尔调整对 HIV 发病率的估计没有净影响，因为假阳性恰好抵消了假阴性。哈格罗夫调整存在数学错误，可能会导致 HIV 发病率显着低估，特别是在存在大量长期流行感染的情况下。结论：此处评估的生物标志物发病率估计的两个调整程序旨在纠正错误分类，但不会提高准确性，并且在某些情况下可能会引入显着偏差。相反，可以通过改进所研究人群的平均窗口期的估计和横截面样本的代表性来提高生物标志物估计的准确性。发病率的队列估计也受到重要的误差来源的影响，不应盲目地将其视为评估生物标志物估计有效性的金标准。 (C) 2009 年 Wolters Kluwer Health 垂直条 Lippincott Willianis & Wilkins
objective: To evaluate adjustment procedures that have been proposed to correct HIV incidence rates derived from cross-sectional surveys of biomarkers (e.g. BED capture enzyme immunoassay). These procedures were motivated by some reports that the biomarker BED approach overestimates incidence when compared to cohort studies.Design: Consideration of the Hargrove and McDougal adjustment procedures that adjust biomarker estimates of HIV incidence rates for misclassification with respect to the timing of infections.Methods: Performed mathematical and statistical analysis of the adjustment formulas. Evaluated sources of error in cohort studies of incidence that could also explain discrepancies between cohort and biomarker estimates.Results: The McDougal adjustment has no net effect on the estimate of HIV incidence because false positives exactly counterbalance false negatives. The Hargrove adjustment has a mathematical error that can cause significant underestimation of HIV incidence rates, especially if there is a large pool of prevalent long-standing infections.Conclusion: The two adjustment procedures of biomarker incidence estimates evaluated here that purport to correct for misclassification do not increase accuracy and in some situations can introduce significant bias. Instead, the accuracy of biomarker estimates can be increased through improvements in the estimates of the mean window period of the populations under study and the representativeness of the cross-sectional samples. Cohort estimates of incidence are also subject to important Sources of error and should not blindly be considered the gold standard for assessing the validity of biomarker estimates. (C) 2009 Wolters Kluwer Health vertical bar Lippincott Willianis & Wilkins