Methods to improve the detection of treatment effects in ARDS clinical trials

 描述（由申请人提供）：每年约有14万美国人患急性呼吸窘迫综合征（ARDS），其中40%的患者死亡。然而，在所有旨在检测死亡率降低的ARDS随机临床试验（RCT）中，只有5%能够确定新的成功干预措施。除了提高生存率外，2009年NHLBI研讨会还确定了未来ARDS RCT的几个优先事项，包括制定策略以招募更少的患者，并关注ARDS幸存者的长期以患者为中心的终点。尽管重症监护社区试图确定干预措施对重症监护病房（ICU）后终点的影响，如生活质量（QOL）以及身体和认知功能，但在评估ARDS患者的非死亡终点方面存在一些方法学挑战。这些包括由于患者死亡率高而导致的数据缺失（即，从死亡中删失），ARDS患者亚群之间的异质性治疗效果，以及缺乏以患者为中心的长期结局的经验证的早期替代终点。本研究和培训计划的总体目标是执行三项相互关联的研究，作为临床流行病学博士论文的主题，这将直接关系到改善ARDS患者RCT结果的执行、分析和应用。首先，在目标1中，我们将提出一个创新的统计框架，用于分析短期和长期的非致命结果，解决由死亡引起的数据缺失造成的偏差。具体来说，我们将利用具有纵向和生存分量的联合建模框架，从而获得统计功效（可能减少 RCT中所需的样本量），同时提供比RCT中常用的当前方法更大的临床和统计推断。接下来，在目标2中，我们将基于患者水平的数据生成多变量风险评分，该评分可用于指导异质性ARDS患者的更个性化的治疗建议，并将其整合到我们的联合建模框架中。最后，在目标3中，我们将寻求确定ICU通气暴露的阈值效应（例如，需要机械通气的时间，无呼吸机天数）预测长期 死亡率和ICU后QOL。一个严格的课程，包括教学和经验学习重症监护，统计学和先进的流行病学将轮申请人的培训，准备他是一个独立的和协作的教师调查和重症监护trialis在完成他的博士学位。

 DESCRIPTION (provided by applicant): Each year, approximately 140,000 Americans develop acute respiratory distress syndrome (ARDS), and 40% of these patients die. Yet, only 5% of all ARDS randomized clinical trials (RCTs) designed to detect reductions in mortality have been able to identify new, successful interventions. In addition to improving survival, a 2009 NHLBI workshop identified several priorities for future ARDS RCTs, including the development of strategies to enroll fewer patients and a focus on longer-term patient-centered endpoints for ARDS survivors. Although the critical care community has sought to identify interventions' effects on post intensive care unit (ICU) endpoints such as quality of life (QOL) and physical and cognitive functioning, there are several methodological challenges in assessing non-mortal endpoints among ARDS patients. These include missing data resulting from high patient mortality rates (i.e., censoring from death), heterogeneous treatment effects among subsets of ARDS patients, and the lack of validated, early surrogate endpoints for long-term patient-centered outcomes. The overall goal of this research and training plan is to execute three interrelated studies as the subject of a doctoral dissertation in clinical epidemiology that will have immediate relevance in improving the conduct, analysis and application of findings from RCTs for ARDS patients. First, in Aim 1, we will advance an innovative statistical framework for the analysis of short- and long-term non-mortal outcomes that addresses the biases resulting from death-induced missing data. Specifically, we will utilize a joint modeling framework with longitudinal and survival components, resulting in gains in statistical power (potentially reducing the needed sample sizes in RCTs), while offering greater clinical and statistical inference than current methods commonly used in RCTs. Next, in Aim 2, we will generate a multivariable risk score based on patient-level data that can be used to guide more individualized treatment recommendations among heterogeneous ARDS patients, and integrate it into our joint modeling framework. Finally, in Aim 3, we will seek to identify threshold effects of ICU ventilation exposure (e.g., time requiring mechanical ventilation, ventilator-free days) that predict long-term mortality and post-ICU QOL. A rigorous curriculum including didactic and experiential learning in critical care, statistics, and advanced epidemiology will round out the applicant's training, preparing him to be an independent and collaborative faculty investigator and critical care trialis at the completion of his PhD.