RESEARCH METHODS CORE

RMC提出的工作将推动对干预至关重要的领域的统计方法 学习。仔细设计干预研究非常重要，以便于了解 正在研究的干预措施。通过适当和仔细的设计，可以得出更可靠的结论。在RMC中， 我们建议将研究人员聚集在一起，他们正在解决一些对 评价该中心拟议的试点干预和评估举措以及支持的RO1 将从这些试点举措演变而来的有效性试验。 学习损耗困扰着许多研究，因为跟踪所有研究变得越来越困难 受试者：需要新的研究设计来减少磨耗对研究结果的影响。长期随访 往往是必要的，以确定预防性干预的长期效果，例如良好的行为 JHU PIRC研究的游戏和路径+GBG干预。但这种长期的随访导致了在 处理学习自然减员问题。大多数统计工作都把重点放在统计上，制定了处理自然减员的方法 分析，例如加权或推算方法，以调整丢失的数据(Little&Rubin，2002；Groves 等人，2004年)。然而，在某些情况下，更好的研究设计和仔细选择后续研究的受试者可以减少 在分析阶段需要复杂的建模假设(Brown等人，2000年；Graham等人，2001年)。 然而，为了充分理解这些设计的好处，还需要进一步的方法学工作。 作为早期预防方案结果的经济影响的重要性，以及时间长短 需要遵守它们，也给这些计划的经济评估(AOS)带来了特殊的挑战 等人，2004年；Kellam和Langevin，2003年)。虽然一些纵向研究跟踪了治疗和控制 早期干预的受试者时间过长(Barnett，1996；Maase&Barnett，2003)，做这样的长期干预 由于在更长的时间内获得高响应率所涉及的成本，跟踪通常是困难的 一段时间。由于非随机等因素，长期随访也给分析带来了挑战 样本流失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措施的影响 利用关于更近的经济结果及其关系的信息，对远距离经济结果进行干预 近端和远端结果之间的差异。这有可能降低对长期影响的成本预测 早期预防干预的重要性。然而，还需要做更多的工作来充分开发这些方法并确定何时 远端预测将是适当的。 重要的是要检测由后随机化调节的干预反应的变化 变量。JHU(如Lalongo等人，1999)和其他地方(如Reid等人，1999)的干预研究表明 这种影响的变化几乎和显著的主效应一样频繁(Brown&Liao，1999)。一种改进的 了解干预反应的亚组差异及其影响因素将有助于 设计预防性和早期干预措施，更准确地针对那些未能从现有的 干预措施。干预研究人员未能解决与结果差异相关的问题，原因是 部分原因是我们检查亚群变异的统计程序的局限性。改进的分析策略和 如果我们要了解亚群的差异和因素，就需要更广泛地传播这些策略 对此贡献良多。RMC成员之前的工作已经详细研究了如何检测子组变异 干预反应受后随机化变量控制(Jo，2002a-c)。这项工作建立在 Frangakis&Rubin(2002)提出的主要分层框架。还需要考虑进一步的工作 对治疗后调解人本身进行纵向测量的环境，例如遵从性行为 随着时间的推移。区域协调委员会成员的工作将在这一重要方向上扩展他们以前在这一领域的工作。 政策制定者需要确定在随机试验样本中看到的结果是否可能 推广到目标人群，这可能与试验样本有所不同。连 有效性试验很少使用完全代表目标人群的受试者进行，其中 正在评估的干预措施可能最终会得到实施(Rothwell，2005)。用统计方法来评估 正如最近强调的那样，需要将有效性试验的结果推广到这些目标人群 政府报告(国家精神卫生研究所，1999年；医学研究所，2006年)。在本报告中提出的工作 RMC将在RMC成员正在进行的研究(Frangakis&Rubin，2002；Stuart 2007b)的基础上开发 这样的方法，弥合了内部和外部的有效性。相辅相成的工作将延长“目标效率” 由RMC成员开发的方法(Salkever等人，2008年)，这些方法考虑了 采取预防性干预措施，使其惠及最受益的个人。这些努力将指导 由中心调查人员进行的研究的设计和实施。

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.