Causal inference in paired two-arm experimental studies under noncompliance with application to prognosis of myocardial infarction

A latent variable approach to potential outcomes for emergency department admission decisions

In emergency departments (EDs), care providers continuously weigh admissions against continued monitoring and treatment often without knowing their condition and health needs. To understand the decision process and its causal effect on outcomes, an observational study must contend with unobserved/missing information and a lack of exchangeability between admitted and discharged patients. Our goal was to provide a general framework to evaluate admission decisions from electronic healthcare records (EHRs). We describe admission decisions as a decision-making process in which the patient's health needs is a binary latent variable. We estimate latent health needs from EHR with only partial knowledge of the decision process (ie, initial evaluation, admission decision, length of stay). Estimated latent health needs are then used to understand the admission decision and the decision's causal impact on outcomes. For the latter, we assume potential outcomes are stochastically independent from the admission decision conditional on latent health needs. As a case study, we apply our approach to over 150 000 patient encounters with the ED from the University of Michigan Health System collected from August 2012 through July 2015. We estimate that while admitting a patient with higher latent needs reduces the 30-day risk of revisiting the ED or later being admitted through the ED by over 79%, admitting a patient with lower latent needs actually increases these 30-day risks by 3.0% and 7.6%, respectively.

Reporting of Baseline Characteristics to Accompany Analysis by Instrumental Variables

A property and criticism of instrumental variable (IV) estimators is that they estimate the exposure effect in the compliers, individuals whose exposure is influenced by the instrument. It is conceivable that the exposure effect is different in individuals whose exposure is not influenced by the instrument. For that reason, it is useful to know who the compliers are. We present methods for reporting averages and other statistics and comparing them between the compliers and noncompliers, which are applicable to scenarios in which the instrument and exposure are fixed in time and dichotomous. The methods are illustrated in a comparison of outcomes between teaching and nonteaching hospitals in patients undergoing brain or spinal surgery.