Estimation of Causal Effects Using Instrumental Variables with Nonignorable Missing Covariates: Application to Effect of Type of Delivery Hospital on Premature Infants

• Activity Number: 526
• Type: Topic Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Fan Yang*+ and Scott Lorch and Dylan S Small
• Companies: University of Pennsylvania and Children's Hospital of Philadelphia and University of Pennsylvania
• Keywords: instrumental variable ; causal inference ; sensitivity analysis ; nonignorable missing data

Abstract

In this article, we develop a method to estimate the causal effect of a treatment using an instrumental variable when there are nonignorable missing covariates, where we allow the missingness of covariates to depend on the fully observed outcome as well as the compliance status which is partially observed. We propose an EM algorithm to estimate the complier average causal effect, and evaluate its behavior under three different missingness mechanisms by comparison with complete case analysis and multiple imputation by chained equations method. We apply our method to an observational study of neonatal care that aims to estimate the delivery effect on mortality of premature babies being delivered in a high-level NICU vs. a lower-level NICU. Our result demonstrates that high-level NICUs significantly reduce deaths for babies of small gestational age whereas for almost mature babies like 37 weeks, the level of NICU makes little difference. A sensitivity analysis is conducted to assess our causal conclusions to key assumptions about the missing covariates.