Improving Risk Estimation in Observational Research on Child Maltreatment: Establishing Methods for the Effective Control of Contamination

项目摘要 复制和再现性失败，表现为效应的显著性和幅度的变化 在前瞻性队列研究中检查的特定结局的规模估计，削弱了因果关系， 关于虐待儿童对公共卫生影响的推论。污染，当受试者入组 比较条件是在进入研究之前或在纵向随访期间暴露儿童虐待， 这两种情况在儿童虐待研究中很常见，也是造成儿童虐待的重要性和 当组间差异确实存在时，通过最小化组间差异来估计效应量的大小。尽管 这些影响，有没有既定的方法来控制污染的儿童虐待 research.这是第一次，该应用程序将测试多种控制污染的策略， 对受虐待儿童群体的前瞻性队列研究。调查队将做到这一点 通过对虐待儿童和虐待儿童纵向研究的现有数据进行二次分析， （LONGSCAN; N=1354），一项从出生到18岁的儿童虐待的前瞻性队列研究。一 官方病例记录和自我报告评估的多方法方法， 儿童发展将用于最大限度地提高检测污染的灵敏度，并建立其 LONGSCAN中的流行率。两种控制观察性研究中偏倚的创新方法， 稳健的倾向评分和增强的合成对照，将用于控制 LONGSCAN队列。这两种方法带来了巨大的潜力，因为它们提供了独特的优势， 在研究入组时和整个纵向随访期间控制污染。表演Double 稳健的倾向评分和增强的合成控制模型将根据以下模型进行基准测试：1） 不控制污染，2）通过从统计分析中删除受试者来控制污染，以及3） 控制污染，将其估计为虐待儿童效应的协变量或调节因子。这 综合建模方法在评估 控制污染的每种方法，平衡对所需样本量的限制，对统计的影响 权力，以及风险估计的重要性和幅度的变化。考虑到更大的目标， 方法提供了最准确的估计儿童虐待在不同的经验条件下，这 应用程序将使用模拟的数据结构来评估所有五个模型的结果， LONGSCAN并扩展到未来前瞻性队列研究中最可能遇到的情况，例如 污染发生率和样本量的变化。有效控制污染将加强 关于虐待儿童对公共卫生影响的因果推断，同时最有可能 服务于多个关键利益相关者，包括进行虐待儿童研究的科学家以及儿童 福利政策制定者决定何时向遭受虐待的儿童和家庭提供服务。

PROJECT ABSTRACT Replication and reproducibility failures, as evidenced by variation in the significance and magnitude of effect size estimates for specific outcomes examined across prospective cohort studies, have weakened causal inferences about the public health impact of child maltreatment. Contamination, when subjects enrolled in a comparison condition are exposed child maltreatment prior to study entry or during longitudinal follow-up, is both common in child maltreatment research and a major contributor to variation in the significance and magnitude of effect size estimates by minimizing between-group differences when they truly exist. Despite these implications, there are no established methods for controlling contamination in child maltreatment research. For the first time, this application will test multiple strategies for controlling contamination in prospective cohort research with the child maltreatment population. The Investigative Team will achieve this goal via secondary analysis of existing data from the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN; N=1354), a prospective cohort study of child maltreatment from birth through age eighteen. A multi-method approach of official case records and self-report assessments measured repeatedly across child development will be used to maximize sensitivity for detecting contamination and establishing its prevalence in LONGSCAN. Two innovative methods for controlling bias in observational research, doubly robust propensity score and augmented synthetic controls, will be used to control contamination in the LONGSCAN cohort. These two methods bring significant potential as they offer unique advantages for controlling contamination at study entry and throughout longitudinal follow-up. The performance of doubly robust propensity score and augmented synthetic control models will be benchmarked against models that: 1) do not control contamination, 2) control contamination by removing subjects from statistical analysis, and 3) control contamination by estimating it as a covariate or moderator of child maltreatment effects. This comprehensive modeling approach uses statistical efficiency principles when evaluating the performance of each method for controlling contamination, balancing constraints on needed sample size, impact on statistical power, and change in the significance and magnitude of risk estimates. Given the larger goal to identify which methods provide the most accurate estimates of child maltreatment under different empirical conditions, this application will evaluate results from all five models using simulations that both mimic the data structure of LONGSCAN and extend to conditions most likely encountered in future prospective cohort research, such as variations in contamination prevalence and sample size. Effectively controlling contamination will strengthen causal inferences about the public health impact of child maltreatment while having the greatest potential to serve multiple, key stakeholders, including scientists conducting child maltreatment research as well as child welfare policy makers deciding when to allocate services to children and families exposed to maltreatment.