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Observational studies of prescription medications and other medical interventions based on administrative data are increasingly used to inform regulatory and clinical decision making.  The validity of such studies is often questioned, however, because the available data may not contain measurements of important prognostic variables that guide treatment decisions.  Recently, approaches to this problem have been proposed that use instrumental variables (IV) defined at the level of an individual health care provider or aggregation of providers.  Implicitly, these approaches attempt to estimate causal effects by using differences in medical practice patterns as a quasi-experiment.   Although preference-based IV methods may usefully complement standard statistical approaches, they make assumptions that are unfamiliar to most biomedical researchers and therefore the validity of such an analysis can be hard to evaluate.  Here, we describe a simple framework based on a single unobserved dichotomous variable that can be used to explore how violations of IV assumptions and treatment effect heterogeneity may bias the standard IV estimator with respect to the average treatment effect in the population.  This framework suggests various ways to anticipate the likely direction of bias using both empirical data and commonly available subject matter knowledge, such as whether medications or medical procedures tend to be overused, underused, or often misused.  This approach is described in the context of a study comparing the gastrointestinal bleeding risk attributable to different non-steroidal anti-inflammatory drugs.

Preference-Based Instrumental Variable Methods for the Estimation of Treatment Effects: Assessing Validity and Interpreting Results