Research Methods Core

研究方法核心（RMC）的使命是在“真实的”中提高研究的质量和影响力。 通过应对临床和服务提供带来的方法学挑战， 老年抑郁症的复杂性，获得良好护理的机会有限。我们自己的社区提出的关切 研究，以及更广泛的领域，指导了RMC倡议的发展，以及我们的一个 发展研究和我们的三项试点研究： 倡议1：干预措施制定结果的预测者和调节者： 对可能对现有治疗方法无反应的抑郁老年人的新干预措施是一种 复杂的过程我们将依赖于我们丰富的RCT数据库的探索性分析，并使用 治疗反应的结果和调节因素，以加强对目标人群的选择，改善我们的 干预措施，并加快将研究转化为实践的进程。试点三注重可行性 （c）将网上培训作为加强传播社区干预措施的一种方式。试点5项研究 家庭保健护理管理的成本作为评估其可行性的一种方式。 倡议2.减少偏倚：干预措施侧重于提供者行为和患者结局 通常使用群集（基于设置）随机化设计，导致非等效组。充分发挥两国 患者RCT的倾向性建模研究，我们将研究倾向性建模在 在基于聚类的设计中调整组差异（也在发展项目4中讨论）。 偏倚的另一个来源是受试者流失造成的数据丢失，这是干预研究中固有的问题， 老年抑郁症等慢性疾病为了解决这个问题，我们提出了一个连续的，多个 插补程序，建立在我们的工作中，我们使用预测模型评估偏倚减少 基于对不完整、非随机缺失（MNAR）数据分析的多重插补（试点4）。 倡议3.社区产生的干预措施的普遍性：为了解决 基于社区的研究，我们提出了与机构内普遍性相关的程序（例如， 减少患者或客户招募过程引入的样本偏倚的来源和影响），以及 跨机构变化（例如，在选择基于社区的研究的过程中引入了偏见 合作伙伴或执行干预措施）。

The mission of the Research Methods Core (RMC) is to advance the quality and impact of research in "real world settings" by addressing methodological challenges posed by the clinical and service delivery complexities of depressed elders with limited access to good care. Concerns raised by our own communitybased studies, and the field more broadly, guided the development of RMC Initiatives as well as one of our Developmental Studies and three of our Pilot Studies: Initiative 1: Predictors and Moderators of Outcomes in Intervention Development: Development of novel interventions for depressed seniors who may not respond to existing treatment approaches is a complex process. We will rely on exploratory analyses of our rich RCT databases and use predictors of outcomes and moderators of treatment response to sharpen the selection of target populations, improve our interventions, and accelerate the process of moving research into practice. Pilot 3 focuses on the feasibility of web-based training as a way of enhancing the dissemination of community interventions. Pilot 5 studies the cost of care management in home healthcare as a way of evaluating its feasibility. Initiative 2. Reduction of Bias: Interventions focusing on provider behavior as well as patient outcomes often use cluster (setting-based) randomization designs resulting in non-equivalent groups. Building on our study of propensity modeling for patient-RCTs, we will investigate the suitability of propensity modeling in adjusting for group differences in cluster based designs (also addressed in Developmental Project 4). Another source of bias is loss of data through subject attrition, a problem inherent in intervention studies of chronic disorders like geriatric depression. To address this problem, we propose a sequential, multiple imputation procedure that builds on our work in which we evaluated bias reduction using a predictive model based on multiple imputation for the analysis of incomplete, missing not at random (MNAR) data (Pilot 4). Initiative 3. Generalizability of Community-Generated Interventions: To address sampling bias in community-based studies, we propose procedures related to both within-agency generalizability (e.g., reducing the source and impact of sample bias introduced by patient or client recruitment processes) and across-agency variation (e.g., biased introduced in the process of selecting community-based research partners or in implementing interventions).