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

项目摘要 复制和再现性故障，从影响的重要性和大小的变化中可以看出 通过前瞻性队列研究检查的特定结果的规模估计，削弱了因果关系 关于虐待儿童对公众健康影响的推论。污染，当受试者登记在 对照条件是在进入研究之前或在纵向随访期间暴露的虐待儿童情况，是 两者在虐待儿童的研究中都很常见，也是导致意义和 通过最小化组间差异(如果它们确实存在)来估计效应大小。尽管 这些影响，没有既定的方法来控制虐待儿童中的污染 研究。这个应用程序将第一次测试多种控制污染的策略 虐待儿童人群的前瞻性队列研究。调查小组将实现这一目标 通过对儿童虐待和忽视纵向研究的现有数据进行二次分析来实现目标 (LONGSCAN；N=1354)，一项关于从出生到18岁虐待儿童的前瞻性队列研究。一个 采用多种方法对官方案例记录和自我报告评估进行重复评估 将利用儿童发展来最大限度地提高检测污染的敏感度，并建立其 龙坎的流行率。观察性研究中控制偏差的两种创新方法 强健的倾向评分和增强的合成控制将用于控制 龙山军团。这两种方法带来了巨大的潜力，因为它们为 在研究开始时和整个纵向随访期间控制污染。双倍的表现 稳健的倾向评分和增强的合成控制模型将以下列模型为基准：1) 不控制污染，2)通过从统计分析中去除对象来控制污染，以及3) 通过估计污染作为虐待儿童影响的协变量或调节因素来控制污染。这 综合建模方法使用统计效率原理来评估系统的性能 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大小的变化。考虑到更大的目标是确定哪些 方法提供了不同经验条件下对虐待儿童的最准确的估计，这 应用程序将使用模拟以下两种数据结构的模拟来评估所有五个模型的结果 并扩展到未来前瞻性队列研究中最有可能遇到的情况，例如 污染流行率和样本量的变化。有效控制污染将加强 虐待儿童对公共健康的影响的因果推断，同时最有可能 为多个关键利益攸关方提供服务，包括开展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分配服务。

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.