Institute for Interdisciplinary Salivary Bioscience Research, University of California, Irvine, CA, USA; Department of Psychological Science, University of California, Irvine, CA, USA.
Institute for Interdisciplinary Salivary Bioscience Research, University of California, Irvine, CA, USA.
Psychoneuroendocrinology. 2021 Jun;128:105203. doi: 10.1016/j.psyneuen.2021.105203. Epub 2021 Mar 17.
Best practice standards for measuring analyte levels in saliva recommend that all biospecimens be tested in replicate with mean concentrations used in statistical analyses. This approach prioritizes minimizing laboratory-based measurement error but, in the process, expends considerable resources. We explore the possibility that, due to advances in salivary assay precision, the contribution of laboratory-based measurement error in salivary analyte data is very small relative to more important and meaningful variability in analyte levels across biological replicates (i.e., between different specimens). To evaluate this possibility, we examine the utility of the repeatability intra-class correlation (rICC) as an additional index of salivary analyte data precision. Using randomly selected subsamples (Ns=200 and 60) of salivary analyte data collected as part of a larger epidemiologic study, we compute the rICCs for seven commonly assayed salivary measures in biobehavioral research - cortisol, alpha-amylase, c-reactive protein, interlekin-6, uric acid, secretory immunoglobulin A, and testosterone. We assess the sensitivity of rICC estimates to assay type and the unique distributions of the underlying analyte data. We also use simulations to examine the bias, precision, and coverage probability of rICC estimates calculated for small to large sample sizes. For each analyte, the rICCs revealed that less than 5% of variation in analyte levels was attributable to laboratory-based measurement error. rICC estimates were similar across all analytes despite differences in analyte levels, average intra-assay coefficients of variation, and in the distributional properties of the data. Guidelines for calculating rICC are provided to enable investigators and laboratory staff to apply this metric and more accurately quantify, and communicate, the magnitude of laboratory-based measurement error in their data. By helping investigators scale measurement error relative to more scientifically meaningful variability between biological replicates, the application of the rICC has the potential to influence research strategies and tactics such that resources (e.g., finances, effort, number/volume of biospecimens) are allocated more efficiently and effectively.
测量唾液中分析物水平的最佳实践标准建议所有生物标本都要进行重复测试，统计分析中使用平均值。这种方法优先考虑最小化实验室测量误差，但在此过程中会消耗大量资源。我们探讨了这样一种可能性，由于唾液分析精度的提高，与生物重复（即不同标本）之间分析物水平的更重要和有意义的变异性相比，实验室测量误差对唾液分析物数据的贡献非常小。为了评估这种可能性，我们检查了重复性组内相关系数 (rICC) 作为唾液分析物数据精度的附加指标的效用。使用作为更大的流行病学研究的一部分收集的唾液分析物数据的随机选择子样本 (Ns=200 和 60)，我们计算了生物行为研究中七种常用唾液测量的 rICCs-皮质醇、α-淀粉酶、C 反应蛋白、白细胞介素-6、尿酸、分泌型免疫球蛋白 A 和睾酮。我们评估了 rICC 估计值对分析类型和潜在分析物数据的独特分布的敏感性。我们还使用模拟来检查 rICC 估计值的偏差、精度和覆盖率，这些估计值是针对小到大的样本量计算的。对于每种分析物，分析物水平变化的不到 5%归因于基于实验室的测量误差。尽管分析物水平、平均内部分析变异系数以及数据的分布特性存在差异，但所有分析物的 rICC 估计值都相似。提供了计算 rICC 的指南，以使研究人员和实验室工作人员能够应用该指标并更准确地量化和传达其数据中基于实验室的测量误差的大小。通过帮助研究人员将测量误差与生物重复之间更具科学意义的变异性进行比较，rICC 的应用有可能影响研究策略和策略，从而更有效地分配资源（例如，财务、努力、生物标本的数量/体积）。
