Hierarchical Bayes Methods for Serial Dilution Assays

描述（由申请人提供）：连续稀释是测量生物样品中未知化合物浓度时广泛使用的关键步骤。低于检测限的测量是这些测定中的一个持续问题。我们将使用分层贝叶斯推断（一种在不确定性存在的情况下估计参数组的统计方法）来改进系列稀释测定的估计，从而允许估计先前被确定为“低于检测限”的浓度。“我们将使用开源软件开发一个程序，以便来自世界各地实验室的研究人员可以评估和使用新方法。我们将在已知条件下进行实验室验证研究，评价新方法。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灰尘样本中过敏原的实验室研究。通过扩展检测限，改进的估计程序将特别有助于儿童哮喘的研究，即使是非常低的过敏原浓度也被假设为具有不良健康影响。我们还将进行一系列的实验和数据分析，以扩展模型，使校准曲线的变化，由于污染的样品，这是一个常见的问题，在环境样品的研究，更普遍的生物测定。我们将探索更有效的设计系列稀释实验使用我们的估计程序的可能性。我们的方法将在我们的过敏原和哮喘的实验室研究的背景下开发;然而，我们预计它将更普遍地适用于许多不同生物背景下的系列稀释测定。一般来说，测量低水平的接触对于解决公共卫生问题至关重要，因此，本项目对不确定性来源进行了明确建模，从而在低水平下进行了更精确的估计，这将有可能导致更有效的生物测定。

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.