Bayesian Mediation Analysis for Neonatal Neurodevelopmental Outcomes in Pregnancy with Opioid Exposure

联系PD/PI：文、雪荣 摘要 中介分析有助于量化母亲阿片类药物暴露之间的直接和间接关系 妊娠、新生儿短期不良结局和儿童长期神经发育结局。确实有 估计中介模型的不同方法，与贝叶斯方法相比，贝叶斯方法具有一些优势 频率对应物：包括先验信息的可能性，当一个多水平模型 结构，在分析样本量较小的数据时具有更大的灵活性。在进行最初资助的 项目(1R15HD097588)，我们观察到妊娠阿片类药物暴露对多个短期的显著影响 儿童先天性畸形与长期神经发育结局。然而，目前还不清楚是否 观察到的影响是直接的还是间接的，以及母亲使用阿片类药物对新生儿发育的影响是否仍然存在 经调解因素调整后显著。有限的数据来源限制了我们的知识，创造了一个令人敬畏的 研究机会。在这个项目中，我们将调查母亲使用阿片类药物对多个短时和短时反应的中介作用 对儿童的长期不良妊娠结局。将使用两个相关联的数据集来检查不同的结果， 提供较长的随访时间，并验证妊娠窗口期的估计。我们的发现来自最初的 获奖项目表明，孕妇使用处方阿片类药物与增加 先天性畸形与新生儿神经发育结局。有必要为调解做进一步的调整 影响因素并确定孕期阿片类药物暴露的直接影响。本项目的重点是研究 围产期阿片类药物暴露与儿童不良健康结局的关系。我们假设那张处方 怀孕期间使用阿片类药物直接对儿童的长期发展构成风险，这优化了疼痛 对孕妇的管理改善了儿童的长期健康结果。具体目标1：确定 母亲与新生儿接触处方类阿片类药物的中介因素 神经发育结果。此目标的目标是确定与以下各项有重要关联的调解人 母亲接触阿片类药物，也与儿童长期神经发育结果密切相关。首先，我们将 建立多变量Logistic回归模型，评估新生儿短期不良结局与 在调整基线潜在混杂因素后，宫内阿片类药物暴露。第二，参数和非参数 将评估长期神经发育结果和短期不良新生儿结果的相关性 与宫内阿片类药物暴露显著相关。第三，将根据所有选定的内容计算中介风险分数 调解人。具体目标2：建立和估计长期新生儿的贝叶斯中介模型 神经发育结果首先，我们将制定贝叶斯中介分析模型。的规范。 贝叶斯中介模型需要未知参数的先验分布启发，使用来自 以前的研究，以及响应或中介变量的抽样分布。违约的选择 先验允许与频域方法进行比较。第二，马尔可夫链蒙特卡罗方法(MCMC) 应用于贝叶斯模型的估计。将获得所有感兴趣参数的后验分布，特别是 关于怀孕期间母亲接触阿片类药物的影响。后验分布分析允许提取 兴趣、后验均值、中位数和可信区间的汇总统计。将对结果的稳健性进行评估 不同的优先分配选择。 结果：这项工作将通过准确量化阿片类药物在怀孕期间的安全性 围产期处方阿片类药物暴露与长期神经发育和教育状况的关系 孩子们。 项目摘要/摘要

Contact PD/PI: Wen, Xuerong ABSTRACT Mediation analysis is useful to quantify the direct and indirect relationship among maternal opioid exposure during pregnancy, short-term adverse neonatal outcomes, and long-term neurodevelopmental outcomes in children. There are different approaches to estimate mediation models, and Bayesian methods offer some advantages compared to their frequentist counterparts: possibility of including prior information, computational feasibility when a multilevel model structure is needed, and more flexibility in analyzing data with smaller sample sizes. In conducting the originally funded project (1R15HD097588), we have observed significant effects for pregnancy opioid exposure on multiple short-term congenital malformations and long-term neurodevelopmental outcomes in children. However, it is unclear whether the observed effects are direct or indirect, and whether the effect of maternal opioid usage on neonatal development remains significant after adjusting for mediation factors. The limited data sources restrain our knowledge and creates a formidable research opportunity. In this project, we will investigate mediation effects of maternal opioid use on multiple short- and long-term adverse pregnancy outcomes for children. Two linked datasets will be used to examine different outcomes, provide a long follow-up time, and validate the estimation of pregnancy window. Our findings from the originally awarded project have shown that prescription opioid use in pregnant women is associated with increased risk of congenital malformations and neonatal neurodevelopmental outcomes. It is necessary to further adjust for the mediation factors and determine the direct effects of opioid exposure during pregnancy. The focus of this project is to study the relationship between perinatal opioid exposure and adverse health outcomes in children. We hypothesize that prescription opioid use during pregnancy directly poses risks on long-term development of children, and that optimized pain management in pregnant women improves long-term health outcomes for children. Specific Aim 1: To identify the mediation factors that are on the causal pathway of maternal exposure to prescription opioids and neonatal neurodevelopmental outcomes. The goal of this aim is to identify the mediators that are significantly associated with maternal opioid exposure and also strongly correlated with child long-term neurodevelopmental outcomes. First, we will fit a multivariable logistic regression model to assess the association between short-term adverse neonatal outcomes and in-utero opioid exposure after adjusting for baseline potential confounding factors. Second, parametric and non-parametric correlations will be assessed for long-term neurodevelopmental outcomes and short-term adverse neonatal outcomes that are significantly related to in-utero opioid exposure. Third, a mediating risk scores will be calculated based on all selected mediators. Specific Aim 2: To formulate and estimate a Bayesian mediation model for the long-term neonatal neurodevelopmental outcome First, we will formulate a Bayesian mediation analysis model. The specification of a Bayesian mediation model requires the prior distribution elicitation for the unknown parameters, using information from previous studies when available, and a sampling distribution for the response or mediation variable. The choice of default priors allows for comparison with frequentist methods. Second, Markov Chain Monte Carlo methods (MCMC) will be applied to estimate Bayesian models. Posterior distributions will be obtained for all the parameters of interest, in particular for the effect of maternal opioid exposure during pregnancy. Posterior distribution analysis allows for the extraction of summary statistics of interest, posterior means, medians, and credible intervals. Robustness of results will be assessed for different prior distribution choices. Outcome: This work will significantly impact the field of opioid safety in pregnancy by accurately quantifying the association between perinatal exposure to prescription opioids with long term neurodevelopment and educational status in children. Project Summary/Abstract