Regionalization of intensive care for premature babies refers to a triage system of mothers with high-risk pregnancies to hospitals of varied capabilities based on risks faced by infants. Due to the limited capacity of high-level hospitals, which are equipped with advanced expertise to provide critical care, understanding the effect of delivering premature babies at such hospitals on infant mortality for different subgroups of high-risk mothers could facilitate the design of an efficient perinatal regionalization system. Towards answering this question, Baiocchi et al. (2010) proposed to strengthen an excess-travel-time-based, continuous instrumental variable (IV) in an IV-based, matched-pair design by switching focus to a smaller cohort amenable to being paired with a larger separation in the IV dose. Three elements changed with the strengthened IV: the study cohort, compliance rate and latent complier subgroup. Here, we introduce a non-bipartite, template matching algorithm that embeds data into a target, pair-randomized encouragement trial which maintains fidelity to the original study cohort while strengthening the IV. We then study randomization-based and IV-dependent, biased-randomization-based inference of partial identification bounds for the sample average treatment effect (SATE) in an IV-based matched pair design, which deviates from the usual effect ratio estimand in that the SATE is agnostic to the IV and who is matched to whom, although a strengthened IV design could narrow the partial identification bounds. Based on our proposed strengthened-IV design, we found that delivering at a high-level NICU reduced preterm babies' mortality rate compared to a low-level NICU for $81,766 \times 2 = 163,532$ mothers and their preterm babies and the effect appeared to be minimal among non-black, low-risk mothers.

翻译：早产儿重症监护的区域化是指根据高风险孕妇所面临的风险，将她们分流至不同能力等级医院的分诊系统。由于配备先进重症监护专业知识的高等级医院容量有限，了解在不同高风险孕妇亚组中，在高等级医院分娩对婴儿死亡率的影响，有助于设计高效的围产期区域化系统。为了回答这个问题，Baiocchi等人（2010）提出通过将焦点转向一个较小的、可在工具变量剂量上实现更大分离的配对队列，来强化一个基于超额出行时间的连续工具变量（IV），并应用于基于IV的配对设计中。强化IV改变了三个要素：研究队列、依从率以及潜在依从亚组。在此，我们引入一种非二分模板匹配算法，该算法将数据嵌入一个目标配对随机化鼓励试验中，既保持原始研究队列的忠实性，又强化了IV。随后，我们研究了在基于IV的配对设计中，基于随机化和依赖于IV的偏倚随机化推断，用于样本平均处理效应（SATE）的部分识别界限。该推断偏离了通常的效应比率估计量，因为SATE对IV以及谁与谁配对保持无偏性，尽管强化IV设计可能缩小部分识别界限。基于我们提出的强化IV设计，我们发现，与低等级NICU相比，在高等级NICU分娩使早产儿死亡率降低，涉及$81,766 \times 2 = 163,532$名母亲及其早产儿，且在非黑人、低风险母亲中，这一效应似乎微乎其微。