Identifying the Optimal Methods for Controlling Contamination Bias in Prospective Research on Child Maltreatment

儿童虐待研究中的污染发生在比较条件的成员在参加研究之前或在纵向随访期间暴露于儿童虐待。这一现象引起了严重的科学关注，因为污染将儿童虐待与比较条件之间不良儿童发展风险的实际差异降至最低。目前的项目通过测试在儿童虐待研究中检测和控制污染偏差的不同方法来解决这一问题。具体而言，研究人员将从独立的前瞻性队列研究中获取现有数据，并检查五种不同统计建模方法的性能，以确定哪种方法具有最佳的污染控制。将执行两种减少偏差的建模方法，倾向评分和增强综合控制模型，并将结果与三种传统方法进行比较：不控制污染，通过从统计模型中删除已识别的参与者来控制污染，以及通过测试污染作为协变量或风险估计的调节因子来控制污染。然后，研究人员将使用数据模拟来模拟这五种方法在不同研究条件下的表现，包括污染流行程度、样本量、统计能力和效应大小的变化。最终，在儿童虐待研究中检测和控制污染的最佳方法将传播给科学家，以便未来的风险评估更加准确，从而通过更可靠的科学数据更好地为儿童福利政策提供信息。虽然在儿童虐待研究中进行了测试，但本项目评估的统计模型的结果将可供任何对暴露变量进行观察性研究的科学家使用，从而提高了STEM领域科学结果的可靠性。最后，如何使用这些方法的知识将通过教育本科生、研究生和博士后如何在儿童虐待领域内外的前瞻性队列研究中控制污染，纳入大学一级的STEM教育。当被招募到非暴露比较条件下的受试者已经暴露于正在调查的事件时，污染是观察性研究中的一种方法学现象，通过最小化组差异来向下偏倚效应大小估计，从而增加复制和可重复性失败。目前的项目将评估在儿童虐待人群的前瞻性队列研究中检测和控制污染偏差的多种策略的表现。这将通过对儿童虐待和忽视纵向研究（LONGSCAN； N=1354）和全国儿童和青少年健康调查ii （nscawi； N=5873）队列现有数据的二次分析来实现，每个队列都是对儿童从出生到18岁虐待的多波前瞻性队列研究。将采用多种方法，包括官方病例报告、自我报告和儿童发展过程中获得的照顾者报告评估，以最大限度地提高检测污染的灵敏度，并确定其在这些队列中的流行程度。在观察性研究中控制偏倚的两种创新方法，双稳健倾向评分和增强合成对照，将用于控制这些队列中的污染偏倚。这两个模型的结果将与其他模型进行评估：1)忽略污染，2)通过从统计分析中删除受试者来控制污染，以及3)通过估计污染作为儿童虐待影响的协变量/调节因子来控制污染。根据统计效率原则，所有五个模型的性能将根据所需的样本量、对统计能力的影响以及效应大小估计中的偏差减少程度进行基准测试。考虑到最终目标是确定哪种方法在不同的经验条件下提供最准确的估计，该项目还将使用模拟LONGSCAN和NSCAW-II的数据结构，并扩展到前瞻性队列研究中常见的条件，包括污染流行程度、样本量和效应大小的变化，来评估所有五个模型的结果。这些结果将通过以下方式产生更广泛的科学和公众影响：将结果传播给儿童虐待领域以外的科学家，他们在存在污染的地方进行研究，培训下一代STEM科学家使用检测和控制污染的最佳方法，该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Contamination in child maltreatment research occurs when members of a comparison condition are exposed to child maltreatment prior to enrolling in a study or during longitudinal follow-up. This phenomenon presents a serious scientific concern, as contamination minimizes real differences in the risk for adverse child development between child maltreatment and comparison conditions. The current project addresses this concern by testing different methods for detecting and controlling contamination bias in child maltreatment research. Specifically, investigators will access existing data from independent prospective cohort studies and examine the performance of five different statistical modeling approaches to determine which has the optimal control of contamination. Two bias reduction modeling approaches, propensity score and augmented synthetic control models, will be executed with results compared against three conventional approaches: no control of contamination, controlling contamination by removing identified participants from the statistical model, and controlling contamination by testing it as a covariate or moderator of risk estimates. Investigators will then use data simulations to model the performance of these five methods across different research conditions, including variations in contamination prevalence, sample size, statistical power, and effect size magnitude. Ultimately, the best performing methods for detecting and controlling contamination in child maltreatment research will be disseminated to scientists so that future risk estimates are more accurate, something that can better inform child welfare policy through more reliable scientific data. While tested within child maltreatment research, results from the statistical models evaluated in this project will be available to any scientist conducting observational research on exposure variables, enhancing the reliability of scientific results across STEM domains. Finally, knowledge on how to use these methods will be incorporated into STEM education at the University-level by educating undergraduate, graduate, and post-doctoral fellows on how to control contamination in prospective cohort studies within and outside the area of child maltreatment.Contamination, when subjects recruited to a non-exposure comparison condition have been exposed to the event under investigation, is a methodological phenomenon in observational research that downwardly biases effect size estimates by minimizing group differences, thereby increasing replication and reproducibility failures. The current project will evaluate the performance of multiple strategies for detecting and controlling contamination bias in prospective cohort research with the child maltreatment population. This will be achieved through secondary analysis of existing data from the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN; N=1354) and the National Survey of Child and Adolescent Well-being-II (NSCAWII; N=5873) cohorts, each of which are multi-wave, prospective cohort studies of child maltreatment from birth through age eighteen. A multi-method approach of official case reports, self-report, and caregiver-report assessments obtained across child development will be used to maximize sensitivity for detecting contamination and establishing its prevalence in these cohorts. Two innovative methods for controlling bias in observational research, doubly robust propensity score and augmented synthetic controls, will be used to control contamination bias in each of these cohorts. Results from these two models will be evaluated against alternative models that: 1) ignore contamination, 2) control contamination by removing subjects from statistical analysis, and 3) control contamination by estimating it as a covariate/moderator of child maltreatment effects. Following statistical efficiency principles, the performance of all five models will be benchmarked with respect to needed sample size, impact on statistical power, and extent of bias reduction in effect size estimates. Given the ultimate goal to identify which methods provide the most accurate estimates under different empirical conditions, this project will also evaluate results from all five models using simulations that both mimic the data structures of LONGSCAN and NSCAW-II and extend out to conditions commonly encountered in prospective cohort studies, including variations in contamination prevalence, sample size, and effect size magnitude. These results will have broader scientific and public impacts by: disseminating results to scientists outside the area of child maltreatment who conduct research where contamination is present, training the next generation of STEM scientists in the optimal methods for detecting and controlling contamination, and providing more accurate estimates of the risks associated with child maltreatment to better inform child welfare policy in the U.S.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.