  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.
