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

项目总结 虐待儿童(CM)是一种广泛的风险因素，与发育和 适应不良。然而，在CM的经验及其发展结果中存在着巨大的异质性。几个 该领域最紧迫的发展问题包括探索这种异质性。然而， 调查慢性心肌炎人群的风险异质性需要对高危、难以控制的风险进行敏感的纵向研究。 以足够的能力接触到受试者，以发现在经历和后果上不同的独特亚群 这样的研究是昂贵的、艰巨的和罕见的。该项目旨在解决这一差距。 本项目的总体目标是应用综合数据分析(IDA)--一套有原则的方法 以及用于同时分析从多个数据集中汇集的原始数据的统计技术-AS 一种解决单个CM研究可能无法解决的风险异质性问题的方法 独自一人。该项目将使用IDA来汇集来自7个由NIH资助的CM队列的数据，这些队列使用黄金标准方法 检查跨越生物、心理和社会领域的长期CM后遗症的发展。将原始数据池化 多项CM研究延长了观察下的发育期，产生了更多的异质性 样本，并增加了检验风险异质性的重要来源的统计能力。IDA将产生一个 综合样本(N=2,898)，包括从4岁开始对一系列生物心理社会过程的评估 一直到40岁。IDA数据集将用于解决三个目标：1)确定CM中的异质性 暴露(即暴露的类型、发育时间和慢性化的变化)对 发育后遗症；A2)确定CM幸存者的发育结局轨迹的异质性 并检查CM暴露的哪些特征与特定轨迹相关联；A3)了解CM如何 暴露和随后的发育过程因种族/族裔的异质性而不同。 该项目具有创新性，因为它将利用NIH在CM研究方面的2500万美元投资来解锁 独立研究的限制，创建比任何其他数据更强大和多样化的CM数据源 个人群体，最大限度地发挥外地互补努力的价值。这一贡献将是巨大的。 因为它将有助于解析CM幸存者中的风险异质性，这对于提高 我们的干预。此外，该项目将创建一个集成的CM数据集，该数据集将成为共享的数据资源 对于该领域，导致了超出K01的指数贡献。最后，这项建议将大大 促进私家侦探的职业发展，使其能够朝着成为一名 独立调查员，能够通过创新的方法推动儿童发展和CM领域的发展。 将提供培训-指导，以学习国际开发协会的方法；获得专门知识，以研究风险异质性； 掌握纵向数据分析技能；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.