Improving Causal Inferences in Child and Family Behavioral Research

改善儿童和家庭行为研究中的因果推断

• 批准号: 10495247
• 负责人: ROBERT Earl LARZELERE
• 金额: $ 7.48万
• 依托单位: OKLAHOMA STATE UNIVERSITY STILLWATER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-24 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10495247
• 关键词: Address; Adolescent; Analysis of Covariance; Behavior; Behavioral Research; Child; Child Care; Child Health; Child Rearing; Child Welfare; Confounding Factors (Epidemiology); Critiques; Data; Data Analyses; Data Set; Development; Disadvantaged; Drug Prescriptions; Effectiveness; Family; Fostering; Foundations; Future; Growth; Hospitalization; Human Development; Intervention; Longitudinal Studies; Medical; Mental Depression; Meta-Analysis; Methods; Modeling; Modification; National Institute of Child Health and Human Development; National Longitudinal Survey of Youth; Occupations; Oppositional Defiant Disorder; Outcome; Parents; Pediatric Hospitals; Persons; Pharmaceutical Preparations; Problem behavior; Prognosis; Psychotherapy; Randomized; Research; Residual state; Risk; Scoring Method; Social Sciences; Testing; Therapeutic; Time; Training Programs; Visit; Work; Youth; conduct problem; depressive symptoms; deviant; externalizing behavior; foster care; improved; innovation; longitudinal analysis; longitudinal dataset; next generation; peer; randomized trial; response; simulation; therapy development

Project Summary/Abstract Most empirically supported treatments for conduct problems in children are implemented by parents, but a recent meta-analysis found them to be half as effective now as 50 years ago. Basic parenting research has failed to improve these treatments partly because of its inadequate causal validity. Of the two basic methods for analyzing longitudinal change, ANCOVA-type residualized-score analyses have been preferred for causal estimates, but they are biased by stable pre-existing differences on outcome scores, according to recent critiques. But the alternative, difference-score analysis, has less statistical power and cannot easily test crucial Pretest X Treatment interactions. This study intends to address those disadvantages by testing extensions of a promising innovation called dual-centered ANCOVA to multi-occasion data. This is a modification of Huitema’s quasi-ANCOVA that centers posttest scores (new) as well as pretest scores (quasi) around their pretest group means. Centering pretests make the two change-score results consistent with each other, whereas centering posttest scores around pretest group means then keeps all change scores unchanged. It avoids the bias in standard ANCOVA by duplicating the results of difference-score analyses, but retains ANCOVA’s advantages: more statistical power and ability to test Pretest X Treatment interactions. For “true” causal effects to compare for testing bias, this research simulates two 3-occasion datasets to fit the null hypotheses of each traditional change-score analysis. It also tests longitudinal data on corrective actions by parents or professionals shown to be effective in randomized trials. Depression treatments (medications and therapy), child hospital visits, and timeout will each be tested in two longitudinal datasets. Research questions include: (1) Do analyses across three occasions still get contradictory results from the two standard change-score analyses? (2) Does dual-centered ANCOVA duplicate the results of pure within-person difference-score analyses, parallel to the robust consistency across both change-score analyses after pretest matching? (3) If standard residualized analyses and pretest matching continue to contradict the known effectiveness of these corrective actions, do Pretest X Treatment interactions reconcile the discrepancies by showing effectiveness for the most at-risk cases? (4) Can the identification and control of Time-1 confounds be improved by similar focusing on pure within-person changes and group-mean centering? We expect this study to document biases that explain why all corrective actions appear to be harmful in residualized analyses, whether implemented by parents (disciplinary responses, talking to youth about deviant behavior and peers) or by professionals (therapy, prescription medications, child care subsidies, foster care, job training programs). Removing these pervasive biases have the potential to improve the ability of basic parenting research to approximate valid causal inferences more closely, which is necessary to improve the next generation of parent-implemented psychotherapies for at-risk children.

项目总结/摘要 大多数经验支持的儿童行为问题的治疗方法是由父母实施的，但 最近的荟萃分析发现，它们现在的有效性只有50年前的一半。基本的育儿研究 未能改善这些治疗，部分原因是其因果有效性不足。在两种基本方法中 对于纵向变化的分析，ANCOVA类型的剩余分数分析已被首选为因果关系， 估计，但他们是有偏见的稳定预先存在的差异，结果得分，根据最近的 批评。但另一种方法，即差异分数分析，统计效力较低， 试验前X治疗相互作用。本研究旨在解决这些缺点，通过测试的扩展， 有前途的创新称为双中心ANCOVA多场合数据。这是对Huitema的 准ANCOVA，将后测分数（新）和前测分数（准）集中在前测组周围 手段中心化预测试使两个变化分数结果相互一致，而中心化预测试使两个变化分数结果相互一致， 后测分数在前测组附近意味着然后保持所有变化分数不变。它避免了 通过复制差异分数分析的结果，但保留了ANCOVA的优点： 更多的统计功效和能力来测试预测试X治疗相互作用。 为了比较“真正的”因果效应以检验偏倚，本研究模拟了两个3次数据集，以拟合 每个传统的变化分数分析的零假设。它还测试了关于纠正措施的纵向数据 由父母或专业人士在随机试验中证明有效。抑郁症治疗（药物） 和治疗），儿童医院就诊和超时将分别在两个纵向数据集中进行测试。研究 问题包括：（1）三种情况下的分析是否仍然从两种标准得到矛盾的结果 变化分数分析？(2)双中心协方差分析是否能重复单纯人内分析的结果 差异分数分析，与预测后两种变化分数分析的稳健一致性平行 匹配吗(3)如果标准的剩余分析和预测试匹配继续与已知的 这些纠正措施的有效性，预测试X治疗相互作用是否通过以下方式调和了差异 对高危病例的有效性(4)时间1混淆的识别和控制是否可以 通过类似的关注纯粹的人内变化和群体平均值中心化来改善？ 我们希望这项研究能够记录偏见，解释为什么所有的纠正措施似乎都是有害的， 剩余分析，是否由父母实施（纪律反应，与青年谈论越轨行为， 行为和同伴）或由专业人员（治疗，处方药，儿童保育补贴，寄养， 职业培训计划）。消除这些普遍存在的偏见有可能提高基本能力， 父母的研究，以近似有效的因果推理更密切，这是必要的，以提高 下一代的父母实施的心理治疗的风险儿童。