Several methods have been proposed for partially or point identifying the average treatment effect (ATE) using instrumental variable (IV) type assumptions. The descriptions of these methods are widespread across the statistical, economic, epidemiologic, and computer science literature, and the connections between the methods have not been readily apparent. In the setting of a binary instrument, treatment, and outcome, we review proposed methods for partial and point identification of the ATE under IV assumptions, express the identification results in a common notation and terminology, and propose a taxonomy that is based on sets of identifying assumptions. We further demonstrate and provide software for the application of these methods to estimate bounds. Supplementary materials for this article are available online.

Instrumental variable (IV) methods are increasingly being used in comparative effectiveness research. Studies using these methods often compare 2 particular treatments, and the researchers perform their IV analyses conditional on patients' receiving this subset of treatments (while ignoring the third option of "neither treatment"). The ensuing selection bias that occurs due to this restriction has gone relatively unnoticed in interpretations and discussions of these studies' results. In this paper we describe the structure of this selection bias with examples drawn from commonly proposed instruments such as calendar time and preference, illustrate the bias with causal diagrams, and estimate the magnitude and direction of possible bias using simulations. A noncausal association between the proposed instrument and the outcome can occur in analyses restricted to patients receiving a subset of the possible treatments. This results in bias in the numerator for the standard IV estimator; the bias is amplified in the treatment effect estimate. The direction and magnitude of the bias in the treatment effect estimate are functions of the distribution of and relationships between the proposed instrument, treatment values, unmeasured confounders, and outcome. IV methods used to compare a subset of treatment options are prone to substantial biases, even when the proposed instrument appears relatively strong.

Williams syndrome is a complex syndrome characterized by developmental abnormalities, craniofacial dysmorphic features, and cardiac anomalies. Sudden death has been described as a very common complication associated with anesthesia, surgery, and procedures in this population. Anatomical abnormalities associated with the heart pre-dispose these individuals to sudden death. In addition to a sudden and rapid downhill course, lack of response to resuscitation is another significant feature seen in these patients. The authors report a five-year-old male with Williams syndrome, hypothyroidism, and attention deficit hyperactivity disorder. He suffered an anaphylactic reaction during CT imaging with contrast. Resuscitation was unsuccessful. Previous reports regarding the anesthetic management of patients with Williams are reviewed and the potential for sudden death or peri-procedure related cardiac arrest discussed in this report. The authors also review reasons for refractoriness to defined resuscitation guidelines in this patient population.