Multilevel Propensity Method to Reduce Bias in Comparative Effectiveness Research

描述（由申请人提供）：整群随机临床试验（RCT）和观察性研究均易受基线治疗组失衡的影响。这个问题出现在设计中，例如不使用患者水平随机化的设计。不平衡反过来又会影响治疗组的比较。目的：本申请的目的是开发和评价一种统计方法，该方法将减少纵向比较有效性研究中非等效比较组引入的偏倚。方法：创新的方法，多层次倾向评分匹配，将在模拟研究中进行评估，并应用于情绪障碍的比较有效性研究。这种方法与我们为非随机研究开发和评估新型治疗有效性分析的工作直接连续。本项目有四个目的：目的1：纵向聚类RCT中多水平匹配的评价：进行模拟研究，以评价在聚类数量较少（<20）的纵向聚类RCT中，多水平倾向评分匹配的偏倚降低和信号检测。目标二：应用于归档的随机对照试验数据：使用多水平倾向评分匹配来重新分析来自两个已完成的老年抑郁症随机对照试验（前景和TRIAD研究）的数据。前景研究了减少自杀和抑郁的干预措施。TRIAD评估了护士改善抑郁症护理的干预措施。目标3：纵向观察性研究中多水平匹配的评价：进行模拟研究，以评价在观察数量不足以进行五分位数分层的纵向观察性研究中使用多水平倾向评分匹配的偏倚减少和信号检测。目标4：应用于存档纵向观察数据：在NIMH抑郁症协作研究（CDS）中，使用多水平倾向评分匹配来检查抗抑郁药和情绪稳定剂药物的疗效和安全性，这是一项为期31年的情绪障碍观察性研究。摘要：本项目旨在对一种有前途的统计方法进行全面评价。预计这种方法将减少偏倚并增强两种设计中的信号检测，这有助于比较有效性研究，从而更准确地确定患者的最佳治疗方案。 公共卫生相关性：在公共卫生层面减轻精神疾病的负担需要确定安全有效的治疗方法。为了应对这一挑战，比较有效性研究必须提供指导治疗选择的经验证据。该应用程序的目的是开发一种创新的统计方法，以减少偏倚，从而更准确地确定患者的最佳治疗方案，并更好地告知临床医生最佳实践。

DESCRIPTION (provided by applicant): Cluster randomized clinical trials (RCTs) and observational studies are each vulnerable to treatment group imbalance at baseline. This problem is seen in designs such as these that do not use patient-level randomization. Imbalance, in turn, compromises treatment group comparisons. Objective: The objective of this application is to develop and evaluate a statistical method that will reduce bias introduced by non-equivalent comparison groups in longitudinal comparative effectiveness research. Method: The innovative method, multilevel propensity score matching, will be evaluated in simulation studies and applied to comparative effectiveness research in mood disorders. This approach is in direct continuity with our work that developed and evaluated novel treatment effectiveness analyses for non- randomized studies. There are four aims of the project: Aim 1: Evaluation of Multilevel Matching in Longitudinal Cluster RCTs: Conduct a simulation study to evaluate bias reduction and signal detection with multilevel propensity score matching in longitudinal cluster RCTs where the number of clusters is small (<20). Aim 2: Application to Archival Cluster RCT Data: Use multilevel propensity score matching to reanalyze data from two completed cluster RCTs for geriatric depression, the PROSPECT and TRIAD studies. PROSPECT examined interventions to reduce suicidality and depression. TRIAD evaluated interventions for nurses to improve depression care. Aim 3: Evaluation of Multilevel Matching in Longitudinal Observational Studies: Conduct a simulation study to evaluate bias reduction and signal detection with multilevel propensity score matching in longitudinal, observational studies where the number of observations is not sufficient for quintile stratification. Aim 4: Application to Archival Longitudinal Observational Data: Use multilevel propensity score matching to examine efficacy and safety of antidepressants and mood stabilizer medications in the NIMH Collaborative Depression Study (CDS), a 31 year observational study of mood disorders. Summary: This project is designed to conduct a comprehensive evaluation of a promising statistical methodology. It is anticipated that this method will reduce bias and enhance signal detection in two designs that can contribute to comparative effectiveness research that will more accurately identify the optimal treatment for patients. PUBLIC HEALTH RELEVANCE: Reducing the burden of mental illness at the public health level requires identification of safe and effective treatments. To address this challenge, comparative effectiveness research must provide empirical evidence that guides the selection of treatment. The objective of this application is to develop an innovative statistical method that will reduce bias and, therefore, more accurately identify the optimal treatment for patients and better inform clinicians on best practices.