Manipulating an Instrumental Variable in an Observational Study of Premature Babies: Design, Bounds, and Inference

Abstract

Regionalization of intensive care for premature infants refers to a triage system that directs mothers to hospitals with varying capabilities based on the risks their babies face. Given the limited capacity of highly specialized hospitals, understanding the impact of delivering premature infants at these facilities on infant mortality could facilitate the design of a more efficient perinatal regionalization system. To address this, Baiocchi et al. (2010) proposed strengthening a continuous instrumental variable (IV) in an IV-based matched-pair design by focusing on a smaller cohort that could be 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 strengthens the IV while maintaining fidelity to the original study cohort. We then study randomization-based and IV dose-dependent, biased randomization-based inference for partial identification bounds of the sample average treatment effect. We found that delivering preterm babies at a high-level, as opposed to a low-level, hospital reduced infant mortality rate for 163, 532 mothers, whereas the treatment effect was minimal among a subgroup of non-black, low-risk mothers.

Bo Zhang

Assistant Professor of Biostatistics

My research interests include design of observational studies, instrumental variables, application of causal inference in medicine and applied statistics in general.