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