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

项目摘要 虐待儿童（CM）是一个广泛的风险因素与损害发展， 适应不良然而，CM的经验及其发展结果存在巨大的异质性。几 该领域最紧迫的发展问题之一就是探索这种异质性。然而，在这方面， 调查CM人群的风险异质性需要对高风险、难治性和高风险人群进行敏感的纵向研究。 以足够的把握度接触受试者，以检测在经验和后果方面存在差异的独特亚组 这种研究是昂贵的，艰巨的，和罕见的。该项目旨在弥补这一差距。 本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 和统计技术，用于对从多个数据集汇集的原始数据进行同时分析， 一种方法来解决可能无法通过单个CM研究解决的风险异质性问题 一个人该项目将使用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。合并原始数据， 多个CM研究延长了观察下的发育期，产生了更异质的 样本，并增加了统计能力，以检查风险异质性的重要来源。IDA将产生一个 综合样本（N = 2，898），包括对4岁以上的一系列生物心理社会过程的评估 到40。IDA数据集将用于解决三个目标：A1）确定CM中的异质性 暴露（即，类型、发育时间和暴露的长期性的变化）差异性地影响 发展后遗症; A2）确定CM幸存者发展结果轨迹的异质性 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。 这个项目是创新的，因为它将利用NIH在CM研究中的2500万美元投资来解锁 限制孤立的研究，创建一个汇集的CM数据源，比任何 这是一个单独的群体，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 的字段，导致指数的贡献，延伸超过这个K 01。最后，这一建议将大大 促进PI的职业发展，使他能够朝着成为一名 独立的调查员谁可以通过创新的方法推进儿童发展和CM领域。 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; 掌握纵向数据分析技能;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.