Causal Methods for Mediation and Interaction

描述（由申请人提供）：通过中介和相互作用分析，提出了新的因果方法来理解随机心理健康试验中的随机行为干预。在四项心理健康研究的具体假设的激励下，两个主要目的是：1)通过随机化后的因素，如提供者对治疗指南的依从性和社会支持（即可能的相互作用的调解），帮助解释这些干预措施的作用机制；2)根据随机化后的因素，如医疗合并症和随机干预效果不同的非研究治疗，帮助确定患者亚组。第三个目标涉及传播。所提出的方法放松了当前中介/交互方法的一个强大假设，即假设中介或交互因素没有不可测量的混淆（顺序可忽略性）。我们根据不同的建模方法，对更多的参数模型和敏感性分析进行了其他假设。针对行动目标的机制，我们提出了用于估计规定性和自然直接效应以及自然间接效应的结构平均模型（SMM）的扩展。自然效应为间接效应研究提供了理论基础，并适应了基线干预与基线后行为和过程因素之间的相互作用，因此适合于本研究所代表的有效性研究。我们还将重点开发最有效的权重，在不假定顺序可忽略性的情况下提高精度。对于随机化后的分层目标，使用随机化后的因素和随机基线干预信息，在主分层（PS）方法下确定潜在的主层或亚组。无论何种随机化组，在随机化后因子水平不变的地层中，基线干预效应代表规定的直接效应。我们将SMM和PS方法扩展到多个嵌套的随机化后因素（例如，医生处方和患者用药行为）、纵向结果、二元结果以及随机化后结果影响随机化后依从性行为的情况。上述方法和标准调解/互动程序及其假设将被评估，并与模拟和分析进行比较，以回答来自四个感兴趣的研究的具体假设。

DESCRIPTION (provided by applicant): New causal methods are proposed for understanding randomized behavioral interventions in randomized mental health trials with mediation and interaction analyses. Motivated by specific hypotheses from four mental health studies, the two primary aims are: 1) to aid the explanation of the mechanism of action of these interventions through post-randomization factors such as provider adherence to treatment guidelines and social support (i.e., mediation with possible interactions); and 2) to help identify subgroups of patients based on post-randomization factors such as medical comorbidities and non-study treatments across which randomized intervention effects vary. An additional third aim addresses dissemination. The proposed methods relax a strong assumption of current mediation/interaction methods that assumes no unmeasured confounding for the mediating or interaction factors (sequential ignorability). We make other assumptions involving more parametric models with sensitivity analyses based on different modeling approaches. For the mechanism of action goal, we propose extensions of structural mean models (SMM) for estimating prescriptive and natural direct effects and natural indirect effects. Natural effects are appropriate for the effectiveness research represented by the studies of interest as they provide a theoretical basis for indirect effects and accommodate interactions between baseline interventions and post-baseline behavioral and process factors. We also will focus on developing optimally efficient weights that improve precision without assuming sequential ignorability. For the post-randomization stratification goal, latent principal strata or subgroups are identified under the principal stratification (PS) approach, using post-randomization factors and randomized baseline intervention information. The baseline intervention effect in strata corresponding to constant post-randomization factor levels regardless of randomization arm represent prescribed direct effects. We extend both the SMM and PS approaches to multiple nested post-randomization factors (e.g., physician prescription and patient medication behavior), longitudinal outcomes, binary outcomes, and cases where post-randomization outcomes influence post-randomization adherence behavior. The above methods and standard mediation/interaction procedures and their assumptions will be evaluated and compared with simulations and analyses to answer the specific hypotheses from four studies of interest.