Bayesian exposure-response analysis for immunoassays data with measurement errors

项目摘要 全世界儿童过敏性疾病和哮喘的发病率正在上升。对于敏感的个人 对于过敏性哮喘和鼻炎，持续暴露于室内过敏原会产生症状和加重。尽管有许多流行病学研究，室内过敏原和 哮喘发病率仍然知之甚少。这项研究的目的是开发新的贝叶斯方法和软件来研究室内过敏原浓度与哮喘之间的因果关系 市中心哮喘儿童的发病率。新方法将使用免疫测定中分析的标准数据校正室内过敏原测量中的测量误差，并提供过敏原的估计值。 校准曲线极端末端的浓度，之前已被确定为低于限度 检测。此外，所提出的方法将允许评估非线性的响应关系 单一过敏原和二元健康结果之间的关系，以及共同暴露于 多种过敏原，并允许纳入其他协变量。使用马尔可夫链蒙特卡罗模拟，我们可以 预测暴露于室内过敏原的健康影响，并获得过敏原浓度的估算值 低于检测限的我们将把提出的方法应用于来自纽约市的数据 纽约7-8岁哮喘儿童的邻里哮喘和过敏研究 市我们将提供一个用户友好的R包，实现所提出的方法和一个Shiny应用程序， 允许交互式数据探索。这将使我们的工作更容易获得，可复制和直接有用。 一旦完成，拟议的研究不仅将推进分析免疫测定数据的统计工具 测量误差和对这些数据的确认-响应分析，以及室内过敏原暴露的研究 和哮喘儿童的发病率。

PROJECT SUMMARY The prevalence of allergic diseases and asthma among children is increasing worldwide. For sensitized individuals with allergic asthma and rhinitis, continued exposure to indoor allergens will produce symptoms and exacerbation. Despite numerous epidemiological studies, the exposure-response relationships between indoor allergens and asthma morbidity remain poorly understood. The goal of the proposed research is to develop new Bayesian methods and software to study exposure-response relationships between indoor allergen concentrations and asthma morbidity among inner-city children with asthma. The new methods will correct measurement errors in the indoor allergen measurements using the standards data analyzed in immunoassays and provide estimates of allergen concentrations at the extreme ends of calibration curves that would previously have been identified as below limits of detection. In addition, the proposed methods will allow for assessing nonlinear exposure-response relationships between a single allergen and a binary health outcome as well as the combined health effect of co-exposure to multiple allergens and allow for inclusion of other covariates. Using Markov chain Monte Carlo simulations, we can predict the health effects of exposures to indoor allergens and obtain imputations of the allergen concentrations for those below limits of detection. We will apply the proposed methods to the data from the New York City Neighborhood Asthma and Allergy Study (NAAS) among 7-8 years old asthmatic children living in New York City. We will provide a user-friendly R package that implements the proposed methods and a Shiny app that allows interactive data exploration. This will make our work more accessible, reproducible and directly useful. Once completed, the proposed research will move forward not only statistical tools for analyzing immunoassay data measured with errors and exposure-response analysis of such data but also research in indoor allergen exposure and asthma morbidity among asthmatic children.