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

阿片类药物暴露妊娠期新生儿神经发育结果的贝叶斯中介分析

基本信息

批准号：
10176650
负责人：
Xuerong Wen
金额：
$ 25.67万
依托单位：
UNIVERSITY OF RHODE ISLAND
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-02-01 至 2022-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10176650
关键词：
6 year old Alcohols Animals Anxiety Attention deficit hyperactivity disorder Award Bayesian Method Bayesian Modeling Behavioral Birth Certificates Child Child Development Childhood Classification Clinical Cognitive Congenital Abnormality Data Data Analyses Data Set Data Sources Detection Development Developmental Delay Disorders Educational Assessment Educational Status Ensure Exposure to Fetal safety Funding Goals Health Incidence Infant Health Knowledge Link Logistic Regressions Low Birth Weight Infant Markov chain Monte Carlo methodology Maternal Exposure Mediating Mediation Mediator of activation protein Medical Mental Depression Methods Minor Modeling Mothers Neonatal Neonatal Abstinence Syndrome Neurologist Newborn Infant Opioid Outcome Outcome Study Pain management Parents Pathway interactions Performance Perinatal Perinatal Care Perinatal Exposure Pharmaceutical Preparations Pregnancy Pregnant Women Public Health Records Research Research Design Research Personnel Resources Risk Role Safety Sample Size Sampling Signal Transduction Social Sciences Statistical Data Interpretation Statistical Models Structural Models Testing Time United States Work adverse outcome adverse pregnancy outcome autism spectrum disorder base clinically relevant epidemiology study experience fetal fetal opioid exposure flexibility follow-up improved in utero interest kindergarten malformation maternal opioid use medication safety multilevel analysis neonatal outcome neurobehavioral neurodevelopment neuron apoptosis obstetric outcomes offspring opioid exposure opioid overdose opioid use opioid use in pregnancy overdose death prenatal exposure prescription opioid response safety study screening skills statistics third grade tool

项目摘要

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

联系PD/PI：Wen，Xuerong 抽象的调解分析对于量化母体阿片类药物之间的直接和间接关系很有用怀孕，短期不良新生儿结局以及儿童的长期神经发育结局。有估计调解模型的不同方法，贝叶斯方法与其相比提供了一些优势频繁的对应物：包括先前信息，计算可行性时的可能性需要结构，并且在分析样本量较小的数据方面具有更大的灵活性。在进行原始资助的项目（1R15HD097588），我们观察到对妊娠阿片类药物对多个短期的显着影响儿童的先天性畸形和长期神经发展结果。但是，目前尚不清楚是否观察到的影响是直接或间接的，以及产妇使用对新生儿发育的影响是否仍然存在调整调解因子后有效。有限的数据来源限制了我们的知识，并创造了强大的知识研究机会。在该项目中，我们将研究产妇使用阿片类药物对多个短期和多个短期和儿童的长期不良怀孕结局。两个链接的数据集将用于检查不同的结果，提供长时间的随访时间，并验证妊娠窗口的估计。我们最初的发现授予的项目表明，孕妇的处方阿片类药物使用与增加的风险有关先天性畸形和新生儿神经发育结果。有必要进一步调整调解因素并确定怀孕期间阿片类药物的直接影响。该项目的重点是研究儿童围产期阿片类药物暴露与不良健康结果之间的关系。我们假设处方怀孕期间的阿片类药物使用直接对儿童的长期发育构成风险，并优化孕妇的管理改善了儿童的长期健康状况。特定目标1：确定孕产妇暴露于处方阿片类药物和新生儿的因果途径上的调解因子神经发育结果。该目标的目的是确定与与之显着相关的调解人孕产妇阿片类药物暴露，并且与儿童长期神经发育结局密切相关。首先，我们会的拟合多变量的逻辑回归模型，以评估短期不良新生儿结果与调整基线潜在混杂因素后，utero阿片类药物的暴露。其次，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局的相关性与局内阿片类药物暴露显着相关。第三，将根据所有选定调解人。特定目的2：为长期新生儿制定和估计贝叶斯调解模型神经发育结果首先，我们将制定贝叶斯调解分析模型。一个规格贝叶斯调解模型需要使用来自未知参数的先验分布启发先前的研究，以及用于响应或中介变量的抽样分布。默认的选择先验可以与频繁的方法进行比较。其次，马尔可夫链蒙特卡洛方法（MCMC）将是应用于估计贝叶斯模型。对于所有感兴趣的参数，将获得后验分布，特别是为了在怀孕期间孕产妇阿片类药物暴露的影响。后验分配分析允许提取摘要的统计数据，后验，中值和可靠的间隔。结果的鲁棒性将被评估不同的先前分发选择。结果：这项工作将通过准确量化怀孕的阿片类药物安全领域，从而显着影响围产期暴露于处方阿片类药物具有长期神经发育和教育状况之间的关联孩子们。项目摘要/摘要