Understanding Risk Heterogeneity Following Child Maltreatment: An Integrative Data Analysis Approach.

项目概要 虐待儿童 (CM) 是一个广泛的风险因素，与发育受损和 适应不良。然而，CM 的经历及其发展结果存在巨大的异质性。几个 该领域最紧迫的发展问题之一涉及探索这种异质性。然而， 调查 CM 人群的风险异质性需要对高风险、难以处理的敏感纵向研究 接触到具有足够能力的受试者，以发现经历和后果不同的独特亚组 CM 的研究——此类研究成本高昂、艰巨且罕见。该项目旨在解决这一差距。 该项目的总体目标是应用综合数据分析（IDA）——一套原则性的方法 以及用于对从多个数据集中汇集的原始数据进行同步分析的统计技术，例如 一种解决风险异质性问题的方法，这些问题可能无法通过单独的 CM 研究解决 独自一人。该项目将使用 IDA 汇集来自 7 个 NIH 资助的 CM 队列的数据，这些队列使用黄金标准方法 检查跨生物心理社会领域的长期 CM 后遗症的发展。汇集原始数据 多项 CM 研究延长了观察下的发育期，产生了更加异质的 样本，并提高统计能力来检查风险异质性的重要来源。 IDA 将产生 综合样本 (N = 2,898)，包括对 4 岁起一系列生物心理社会过程的评估 到 40。IDA 数据集将用于实现三个目标：A1) 确定 CM 中的异质性如何 暴露（即类型、发育时间和暴露长期性的变化）会产生不同的影响 发育后遗症； A2) 识别 CM 幸存者发育结果轨迹的异质性 并检查 CM 暴露的哪些特征与特定轨迹相关； A3) 探索 CM 如何 暴露和随后的发育过程因种族/民族异质性而异。 该项目具有创新性，因为它将利用 NIH 对 CM 研究的 2500 万美元投资来解锁 孤立研究的限制，创建比任何研究都更强大和多样化的 CM 数据汇集源 个体群体，最大限度地发挥该领域互补努力的价值。这一贡献将是巨大的 因为这将有助于解析 CM 幸存者的风险异质性，这对于提高 CM 幸存者的精确度是必要的 我们的干预措施。此外，该项目将创建一个综合的 CM 数据集，该数据集将成为共享数据资源 该领域的指数贡献超出了 K01。最后，这个提议将极大地 促进 PI 的职业发展，使他能够朝着成为一名 独立研究者，可以通过创新方法推进儿童发展和 CM 领域的发展。 将提供培训指导以学习 IDA 方法；获得研究风险异质性的专业知识； 获得纵向数据分析技能；并获得团队科学技能。

PROJECT SUMMARY Child maltreatment (CM) is a broad-ranging risk factor associated with compromised development and maladaptation. Yet, there is vast heterogeneity in the experience of CM and its developmental outcomes. Several of the field’s most pressing developmental questions involve exploring such heterogeneity. However, investigating risk heterogeneity in CM populations requires sensitive longitudinal studies of high-risk, hard-to- reach subjects with adequate power to detect unique subgroups who differ in the experience and consequences of CM—such studies are costly, arduous, and rare. This project aims to address this gap. The overall objective of this project is to apply Integrative Data Analysis (IDA)—a principled set of methodologies and statistical techniques used to conduct simultaneous analysis of raw data pooled from multiple datasets—as a method to address questions about risk heterogeneity that may not be addressed through individual CM studies alone. This project will use IDA to pool data from 7 NIH-funded CM cohorts that used gold-standard methods to examine the development of long-term CM sequelae across biopsychosocial domains. Pooling original data from multiple CM studies stretches the developmental period under observation, generates a more heterogenous sample, and increases statistical power to examine important sources of risk heterogeneity. IDA will yield an integrated sample (N = 2,898) that includes assessment of an array of biopsychosocial processes from ages 4 through 40. The IDA dataset will be used to address three aims: A1) determine how heterogeneity in CM exposure (i.e., variation in types, developmental timing, and chronicity of exposure) differentially influences developmental sequelae; A2) identify heterogeneity in the developmental outcome trajectories of CM survivors and examine which features of CM exposure are associated with specific trajectories; A3) explore how CM exposure and subsequent developmental processes differ based on racial/ethnic heterogeneity. This project is innovative because it will leverage $25 million of NIH investment in CM research to unlock the constraints of isolated studies, creating a pooled source of CM data that is more powerful and diverse than any individual cohort, maximizing the value of complementary efforts in the field. This contribution will be significant because it will help to parse risk heterogeneity in CM survivors, which is necessary to improve the precision of our interventions. Further, this project will create an integrative CM dataset that will be a shared data resource for the field, resulting in exponential contributions that extend beyond this K01. Finally, this proposal will greatly enhance the PI’s career development and enable him to advance toward his long-term goal of becoming an independent investigator who can advance the fields of child development and CM via innovative methods. Training-mentorship will be provided to learn IDA methodologies; gain expertise to study risk heterogeneity; acquire skills in longitudinal data analysis; and gain team science skills.