Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies.
Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies.
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DOI：
10.1097/ede.0000000000000460
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发表时间：
2016-05
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影响因子：
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通讯作者：
Philippat C
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文献类型：
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作者：
Perrier F;Giorgis-Allemand L;Slama R;Philippat C
补充数字内容可在文本中找到。对于具有高受试者内时间变异性的化学品，评估现场生物标本中的暴露生物标志物很难估计长时间内的平均水平。目的是表征在生物标志物浓度的受试者内变异性较高时，受试者内生物标本池的能力，以减少由于暴露错误分类造成的偏差。我们考虑了类内相关系数为0.6和0.2的化学物质。在一项模拟研究中，我们假设在给定时间段内尿液化学物质的平均浓度与健康结果有关，并估计了每个受试者随机收集1至50个生物样本评估暴露的研究的偏倚。我们假设一个典型的输入错误。我们使用受试者内池方法和两种测量误差模型（模拟外推和回归校准）研究了相关性，后者需要对每个受试者进行不止一个生物标本的检测。对于连续和二元结果，对于类内相关系数分别为0.6和0.2的化合物，使用一个样本导致衰减偏差分别为40%和80%。对于类内相关系数为0.6的化合物，采用池化、模拟外推和回归校准方法，将偏差限制在10%以内所需的生物标本数分别为6、2和2个（对于类内相关系数为0.2的化合物，这些值分别为35、8和2）。与池化相比，这些方法并没有提高功率。在不增加检测成本的情况下，受试者内合并限制了衰减偏差。与池化方法相比，模拟外推和回归校准进一步限制了偏差，但增加了分析成本。
Supplemental Digital Content is available in the text. For chemicals with high within-subject temporal variability, assessing exposure biomarkers in a spot biospecimen poorly estimates average levels over long periods. The objective is to characterize the ability of within-subject pooling of biospecimens to reduce bias due to exposure misclassification when within-subject variability in biomarker concentrations is high. We considered chemicals with intraclass correlation coefficients of 0.6 and 0.2. In a simulation study, we hypothesized that the chemical urinary concentrations averaged over a given time period were associated with a health outcome and estimated the bias of studies assessing exposure that collected 1 to 50 random biospecimens per subject. We assumed a classical type error. We studied associations using a within-subject pooling approach and two measurement error models (simulation extrapolation and regression calibration), the latter requiring the assay of more than one biospecimen per subject. For both continuous and binary outcomes, using one sample led to attenuation bias of 40% and 80% for compounds with intraclass correlation coefficients of 0.6 and 0.2, respectively. For a compound with an intraclass correlation coefficient of 0.6, the numbers of biospecimens required to limit bias to less than 10% were 6, 2, and 2 biospecimens with the pooling, simulation extrapolation, and regression calibration methods (these values were, respectively, 35, 8, and 2 for a compound with an intraclass correlation coefficient of 0.2). Compared with pooling, these methods did not improve power. Within-subject pooling limits attenuation bias without increasing assay costs. Simulation extrapolation and regression calibration further limit bias, compared with the pooling approach, but increase assay costs.