Poly-Matching Causal Inference for Assessing Multiple Acute Medical Managements of Pediatric Traumatic Brain Injuries

摘要 临床有效性研究（CER）在与紧急医疗服务相关的研究中发挥着核心作用， 儿童（紧急医疗服务中心）。它是在评估干预措施时认真使用现有的最佳证据， 定义为可能导致健康改善的医学治疗、卫生政策或实践模式 护理质量和患者结局。观察性数据更常用于医疗保健评估 系统或复杂的临床实践，而不是随机对照试验（RCT），由于实际或伦理原因。 然而，利用观测数据进行因果推断面临挑战：（1）重要协变量可能分布 治疗方案之间的差异;（2）传统的统计分析缺乏对未测量的 真让人困惑当干预是二分的，倾向评分为基础的调整被广泛用于减少 通过匹配、分层或加权，观察到的协变量引入的混杂偏倚。匹配 是研究人员的热门选择，因为它创建了类似于RCT的数据结构，易于解释， 对结果建模中的错误规范具有鲁棒性。但在方法上存在着一个重大差距，阻碍了 当有多种（两种以上）治疗选择时，匹配设计。这是由于缺乏良好的 匹配算法，以产生良好的匹配集和匹配后推理的复杂性增加。 我们的首要目标是开发一个统计上有效的匹配设计（称为PMD）和随后的 因果推理程序，用于复杂的观察性医疗保健数据库，其中有多个 治疗组或多个时间点的治疗。具体目标：（1）设计一种创新的PMD用于研究 多个治疗组或治疗随时间推移;根据 评估未测量混杂因素的潜在结局框架和敏感性分析策略 （2）评估在不同时间接受创伤护理的严重TBI（sTBI）患者的因果死亡率影响 创伤中心的类型（PL 1 - 1级儿科，AL 1 - 1级成人，ML 1 - 1级混合创伤中心; PL 2- 2级儿科，AL 2 - 2级成人，ML 2 - 2级混合创伤中心）;（3）评估4种方法的有效性 1-2级医疗管理/治疗（ICP监测、头部CT扫描、脑脊液引流， （4）：评估与疾病中心的依从性 不同类型医院的轻度TBI（mTBI）患者的控制和预防（CDC）头部CT指南。 这项研究预计将填补一个关键的差距，在EMSC的研究，通过扩展常用的二分法 多个治疗组的复杂观察性研究的匹配设计。该项目意义重大， 我们提出的方法是创新的，因为它们包括观察到的混杂调整和不可测量的 混淆评估。我们设想，这种通用方法将广泛适用， 有利于政府机构，政策制定者和社会和健康科学研究人员，其中观察 数据经常用于比较结果研究和方案/政策评价。

Abstract Clinical effectiveness research (CER) plays a central role in research related to emergency medical services for children (EMSC). It is the conscientious use of the best available evidence in evaluating interventions, broadly defined as medical treatments, health policies, or practice patterns, which could lead to improvements in health care quality and patient outcomes. Observational data are more often used in the evaluation of healthcare systems or complex clinical practice than randomized controlled trials (RCTs), due to practical or ethical reasons. However, causal inference with observational data faces challenges: (1) Important covariates may be distributed differently between treatment options; (2) Conventional statistical analysis lacks control for unmeasured confounding. When the intervention is dichotomous, propensity score based adjustment is widely used to reduce the confounding bias introduced by observed covariates, through matching, stratification or weighting. Matching is a popular choice among researchers, as it creates data structures similar to RCTs, is easy to interpret, and robust to misspecifications in outcome modeling. But there is a critical methodological gap hindering the use of matching design when there are multiple (more than two) treatment options. This is due to the lack of good matching algorithms to generate well matched sets and the increased complexity of post-matching inference. Our overarching goal is to develop a statistically valid matching design (referred to as PMD) and subsequent causal inference procedures for use with complex observational healthcare databases, where there are multiple treatment arms or treatments over multiple time points. Specific aims: (1) Devise an innovative PMD for studies with multiple treatment arms or treatments over time; Develop causal inference strategies for PMD based on the potential outcome framework and sensitivity analysis strategies for assessing the unmeasured confounding effect; (2) Evaluate causal mortality impact of severe TBI (sTBI) patients who received trauma care at different type of trauma centers (PL1-level 1 pediatric, AL1-level 1 adult, and ML1-level 1 mixed trauma centers; PL2- level 2 pediatric, AL2-level 2 adult, and ML2-level 2 mixed trauma centers); (3) Assess the effectiveness of 4 Tier 1-2 medical management/therapies (ICP monitoring, head CT scan, cerebrospinal fluid drainage, decompressive craniectomy) on sTBI patient mortality; (4): Evaluate compliance with a Centers for Disease Control and Prevention (CDC) head CT guideline for mild TBI (mTBI) patients by different types of hospitals. This study is expected to fill a critical gap in EMSC research by extending the commonly used dichotomous matching design to complex observational studies with multiple treatment groups. This project is significant and our proposed methods are innovative as they include both observed confounding adjustment and unmeasured confounding assessment. We envision that this general-purpose methodology will be widely applicable and can benefit government agencies, policy makers, and social and health science researchers, where observational data are often utilized for comparative outcomes research and program/policy evaluation.