RESEARCH METHODS CORE

研究方法核心

• 批准号: 8375930
• 负责人: NICHOLAS IALONGO
• 金额: $ 46.43万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8375930
• 关键词: Address; Aftercare; Area; Behavior; Behavior Disorders; Child; Communities; Complex; Data; Distal; Early treatment; Economic Models; Economics; Effectiveness; Effectiveness of Interventions; Ensure; Environment; Evaluation; Failure; Generations; Government; Impairment; Incidence; Individual; Institute of Medicine (U.S.); Intervention; Intervention Studies; Learning; Length; Link; Long-Term Effects; Longitudinal Studies; Measures; Mediating; Mediator of activation protein; Mental disorders; Methodology; Methods; Mission; Modeling; National Institute of Mental Health; Outcome; Plague; Policy Maker; Prevalence; Prevention program; Preventive Intervention; Procedures; Provider; Public Health; Randomized; Reporting; Research; Research Design; Research Methodology; Research Personnel; Sampling; Schools; Services; Solutions; Staging; Statistical Methods; Stratification; Students; Study Subject; Subgroup; Subjects Selections; System; Target Populations; Testing; Time; Translating; Variant; Weight; Work; Youth; base; compliance behavior; cost; design; economic impact; economic outcome; effective intervention; effectiveness trial; follow-up; implementation research; improved; indicated prevention; innovation; intervention effect; member; operation; predictive modeling; programs; randomized trial; response; stem; universal prevention

The work proposed by the RMC will advance statistical methodology in areas crucial to intervention studies. It is important to carefully design intervention studies to facilitate learning about the effectiveness of the interventions under study. With appropriate and careful design, more robust conclusions can be made. In the RMC, we propose to bring together researchers who are addressing a number of methodological issues critical to the evaluation of the Center's proposed pilot intervention and assessment initiatives and the RO1 supported effectiveness trials that will evolve out of these pilot initiatives. Study attrition plagues many studies as it becomes more and more difficult to follow up all study subjects; new study designs are needed to reduce the effects of attrition on study results. Long-term followup is often necessary to determine the long-term effects of preventive interventions, such as the Good Behavior Game and PATHS+GBG interventions studied by the JHU PIRC. But this long-term follow-up leads to challenges in dealing with study attrition. Most statistical work developing methods to deal with attrition have focused on statistical analyses, for example weighting or imputation methods to adjust for the missing data (Little & Rubin, 2002; Groves et al., 2004). However, in some cases better study design and careful selection of subjects to follow-up can reduce the need for complex modeling assumptions at the analysis stage (Brown et al., 2000; Graham et al., 2001). However, to fully understand the benefits of these designs, further methodological work is needed. The importance of economic impacts as outcomes of early prevention programs, and the length of time required to observe them, also poses special challenges for economic assessments of these programs (Aos et al., 2004; Kellam and Langevin, 2003). While some longitudinal studies have tracked treatment and control subjects from early interventions overextended periods (Barnett, 1996; Maase & Barnett, 2003), doing such longterm follow-up often is difficult because of the costs involved in obtaining high response rates over an extended period of time. Long-term follow-ups also present challenges to analysis because of factors such as non-random sample attrition One potential solution is to use multiple-stage predictive models to infer impacts of early preventive interventions on distal economic outcomes, using information on more proximal outcomes, and the relationship between the proximal and distal outcomes. This has the potential to allow lower cost predictions of long-term effects of early preventive interventions. However, more work is needed to fully develop the methods and determine when the distal predictions would be appropriate. It is important to detect variation in intervention response that is mediated by post-randomization variables. Intervention research at JHU (e.g., lalongo et al., 1999) and elsewhere (e.g., Reid et al., 1999) suggests that variation in impact is found almost as frequently as significant main effects (Brown & Liao, 1999). An improved understanding of sub-group variation in intervention response and the factors contributing to it would facilitate the design of preventive and early interventions that more precisely target those youth who fail to benefit from existing interventions. The failure of intervention researchers to address issues related to variations in outcomes stems in part from limitations in our statistical procedures for examining subgroup variation. Improved analytic strategies and wider dissemination of these strategies are needed if we are to understand sub-group variation and the factors contributing to it. Previous work by members of the RMC has investigated in detail how to detect subgroup variation in intervention response that is governed by post-randomization variables (Jo, 2002a-c). This work builds on the framework of principal stratification set out by Frangakis & Rubin (2002). Further work is needed to consider settings where the post-treatment mediators are themselves measured longitudinally, such as compliance behavior over time. The work by members of the RMC will extend their previous work in this area in this important direction. Policymakers need ways of determining whether the results seen in randomized trial samples are likely to generalize to target populations, which may be somewhat different from the trial sample. Even effectiveness trials rarely are done using subjects that are fully representative of the target populations in which the interventions being evaluated may eventually be implemented (Rothwell, 2005). Statistical methods to assess the generalizability of results from effectiveness trials to those target populations are needed, as highlighted in recent government reports (National Institute of Mental Health 1999; Institute of Medicine 2006). Work proposed in this RMC will build on research being done by members of the RMC (Frangakis & Rubin, 2002; Stuart 2007b) to develop such methods, bridging internal and external validity. Complementary work will extend the "target efficiency" methods developed by members of the RMC (Salkever et al., 2008), which consider the optimal targeting of preventive interventions so that they reach those individuals whom they will most benefit. These efforts will guide the design and implementation of research conducted by the Center's investigators.

RMC提出的工作将推进对干预至关重要的领域的统计方法 研究。仔细设计干预研究以促进学习有效性很重要 正在研究的干预措施。通过适当而仔细的设计，可以得出更强大的结论。在RMC中， 我们建议将解决许多方法论问题的研究人员聚集在一起 评估该中心提议的飞行员干预和评估计划以及支持的RO1 有效试验将从这些试点计划中发展出来。 研究损耗困扰着许多研究，因为它变得越来越难以跟进所有研究 受试者；需要新的研究设计来减少损耗对研究结果的影响。长期随访 通常是确定预防干预措施的长期影响的必要条件，例如良好行为 JHU PIRC研究的游戏和路径+GBG干预措施。但是，这种长期随访导致了挑战 处理学习流失。大多数统计工作开发用于处理流失的方法都集中在统计上 分析，例如调整丢失数据的加权或插补方法（Little＆Rubin，2002; Groves 等，2004）。但是，在某些情况下，更好的研究设计和仔细选择对后续的受试者可以减少 在分析阶段对复杂建模假设的需求（Brown等，2000； Graham等，2001）。 但是，为了充分了解这些设计的好处，需要进一步的方法论工作。 经济影响作为早期预防计划的结果和时间长度的重要性 需要观察它们，也对这些计划的经济评估提出了特殊挑战（AOS 等，2004； Kellam和Langevin，2003年）。虽然一些纵向研究追踪了治疗和对照 早期干预措施过度延伸期的受试者（Barnett，1996; Maase＆Barnett，2003），经历了如此长期的工作 随访通常很难 一段时间。长期随访也给分析带来了挑战，因为非随机 样本损耗一个潜在的解决方案是使用多阶段预测模型来推断早期预防的影响 对远端经济结果的干预，使用有关更多近端结果的信息以及关系 在近端和远端结果之间。这有可能使长期影响的成本预测较低 早期预防干预措施。但是，需要更多的工作来充分开发方法并确定何时 远端预测是适当的。 检测由后随机化介导的干预响应变化很重要 变量。 JHU的干预研究（例如Lalongo等，1999）和其他地方（例如，Reid等，1999）提出了研究。 这种影响的变化几乎与显着的主要影响一样频繁（Brown＆Liao，1999）。改进 了解干预反应中亚组变化及其造成的因素的理解将有助于 预防和早期干预措施的设计更精确地针对那些未能从现有的年轻人 干预措施。干预研究人员未能解决与结果差异有关的问题。 我们检查亚组变异的统计程序中的局限性。改进的分析策略和 如果我们要了解亚组变异和因素，则需要更广泛的这些策略传播这些策略 为此做出贡献。 RMC成员的先前工作详细研究了如何检测子组变化 在由后机变量控制的干预响应中（Jo，2002a-C）。这项工作建立在 Frangakis＆Rubin（2002）制定的主要分层框架。需要考虑进一步的工作 纵向测量后处理后调解人的设置，例如合规行为 随着时间的推移。 RMC成员的工作将朝着这一重要方向扩展其以前的工作。 决策者需要确定随机试验样本中看到的结果的方法 概括目标人群，这可能与试验样本有些不同。甚至 有效性试验很少是使用完全代表目标人群的受试者进行的 评估干预措施最终可以实施（Rothwell，2005年）。评估统计方法 需要从有效性试验的结果对这些目标人群进行的概括性，如最近所强调的那样 政府报告（国家心理健康研究所，1999年；医学研究所，2006年）。提出的工作 RMC将基于RMC成员（Frangakis＆Rubin，2002; Stuart 2007b）进行的研究建设 这种方法，桥接内部和外部有效性。互补工作将延长“目标效率” RMC成员开发的方法（Salkever等，2008），该方法考虑了最佳靶向的方法 预防性干预措施，以便触及那些最能受益的人。这些努力将指导 中心研究人员进行的研究的设计和实施。