Hierarchical Bayes Methods for Serial Dilution Assays

描述（由申请人提供）：连续稀释是一个关键步骤，广泛用于测量生物样品中未知化合物的浓度。在这些分析中，低于检测限的测量是一个持续存在的问题。我们将使用层次贝叶斯推断——一种在不确定性存在下估计参数组的统计方法——来改进对连续稀释试验的估计，从而允许对先前被确定为“低于检测极限”的浓度进行估计。我们将开发一个使用开源软件的程序，以便世界各地实验室的研究人员能够评估和使用这种新方法。我们将在已知条件下对新方法进行实验室验证研究。我们将立即将该方法应用于从有哮喘风险的儿童家中收集的粉尘样本中的过敏原的实验室研究。通过扩大检测范围，改进的估计程序将特别有助于儿童哮喘的研究，在儿童哮喘中，即使非常低的过敏原浓度也被假设对健康有不利影响。我们还将进行一系列实验和数据分析，以扩展模型，以允许由于样品污染而导致校准曲线的变化，这是环境样品研究和更普遍的生物分析中的常见问题。我们将利用我们的估计程序探索更有效的系列稀释实验设计的可能性。我们的方法将在我们对过敏原和哮喘的实验室研究的背景下发展；然而，我们预计它将更普遍地适用于许多不同生物学背景下的系列稀释试验。一般来说，测量低水平接触对公共卫生问题至关重要，因此，该项目明确模拟了不确定性来源，从而在低水平下得出更精确的估计，这可能会导致总体上更有效的生物测定。

DESCRIPTION (provided by applicant): Serial dilution is a crucial step that is widely used when measuring the concentrations of unknown compounds in biological samples. Measurements below detection limits are a persistent problem in these assays. We will use hierarchical Bayes inference-a statistical approach for estimating groups of parameters in the presence of uncertainty-to improve estimation for serial dilution assays, thus allowing estimation of concentrations that would previously have been identified as "below detection limits." We will develop a program using open-source software so that researchers from laboratories around the world can evaluate and use the new method. We will perform a laboratory validation study evaluating the new method under known conditions. We will immediately apply the methodology to laboratory studies of allergens in dust samples collected from the homes of children who are at risk for asthma. By extending limits of detection, the improved estimation procedure will be particularly helpful in the study of childhood asthma, where even very low allergen concentrations are hypothesized to have adverse health effects. We will also undertake a series of experiments and data analyses to extend the model to allow for changes in the calibration curve due to contamination of the samples, which is a common problem in the study of environmental samples, and in bioassays more generally. We will explore possibilities of more efficient designs of serial dilution experiments using our estimation procedure. Our method will be developed in the context of our laboratory studies of allergens and asthma; however, we anticipate it will be applicable much more generally to serial dilution assays in many different biological contexts. Measurement of low levels of exposure is critical in public health problems in general, hence this project, in which sources of uncertainty are explicitly modeled, leading to more precise estimates at low levels, will potentially lead to more effective bioassays generally.