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Causal Methods for Mediation and Interaction

中介和交互的因果方法

基本信息

批准号：
7888263
负责人：
Marshall M Joffe
金额：
$ 32.39万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-10 至 2012-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7888263
关键词：
Accounting Address Adherence Adverse effects Behavior Behavior Control Behavior Therapy Behavioral Cardiovascular Diseases Clinical Clinical Trials Comorbidity Complex Computer software Data Analyses Depression and Suicide Diabetes Mellitus Documentation Drug Formulations Equation Feeling suicidal Goals Guidelines Health Hormonal Impaired cognition Individual Intervention Intervention Studies Intervention Trial Lead Linear Models Literature Logistic Regressions Measures Mediating Mediation Mediator of activation protein Medical Mental Depression Mental Health Methodology Methods Modeling Non-linear Models Observational Study Online Systems Outcome Pain Participant Pathway interactions Patients Pharmaceutical Preparations Physicians Postmenopause Primary Health Care Procedures Process Provider Randomized Relative (related person)Replacement Therapy Research Personnel Robin bird Scheme Severities Social support Solutions Sterile coverings Stratification Subgroup Suicide Time Training Visit Weight arm base cognitive function comparison group effectiveness research follow-up group intervention improved interest intervention effect post intervention randomized trial simulation treatment as usual treatment effect

项目摘要

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.

描述（由申请人提供）：提出了新的因果方法，用于通过中介和相互作用分析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推动下，两个主要目标是：1）通过随机化后的因素，例如提供者遵守治疗指南和社会支持（即与可能的相互作用进行调解）来帮助解释这些干预措施的作用机制； 2) 根据随机化后因素（例如医疗合并症和非研究治疗）帮助识别患者亚组，其中随机干预效果各不相同。另外的第三个目标涉及传播。所提出的方法放松了当前中介/交互方法的强烈假设，该假设假设中介或交互因素没有不可测量的混杂（顺序可忽略性）。我们做出其他假设，涉及更多参数模型以及基于不同建模方法的敏感性分析。对于行动目标机制，我们提出了结构平均模型（SMM）的扩展，用于估计规定性和自然直接效应和自然间接效应。自然效应适用于以感兴趣的研究为代表的有效性研究，因为它们为间接效应提供了理论基础，并适应基线干预与基线后行为和过程因素之间的相互作用。我们还将专注于开发最佳有效的权重，以提高精度而不假设顺序可忽略性。对于随机化后分层目标，使用随机化后因素和随机基线干预信息，在主要分层（PS）方法下确定潜在主要层或亚组。无论随机化组如何，与恒定的随机化后因子水平相对应的层中的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行为的情况。将评估上述方法和标准中介/交互程序及其假设，并与模拟和分析进行比较，以回答四项感兴趣的研究的具体假设。