A systematic approach to evaluating instrumental variable assumptions: applied example of glucose-lowering medications and risk for hospitalized heart failure in older adults.

One obstacle to adopting instrumental variable (IV) methods in pharmacoepidemiology is their reliance on strong, unverifiable assumptions. We can falsify IV assumptions by leveraging the causal structure, which can strengthen or refute their plausibility and increase the validity of effect estimates. We illustrate a systematic approach to evaluate calendar-time IV assumptions in estimating the known effect of thiazolidinediones on hospitalized heart failure. Using cohort entry time before and after September 2010, when the US Food and Drug Administration issued a safety communication, as a proposed IV, we estimated IV and propensity score-weighted 2-year risk differences (RDs) using Medicare data (2008-2014). We (1) performed inequality tests, (2) identified the negative control IV/outcome using causal assumptions, (3) estimated RDs after narrowing the calendar time range and excluding patients likely associated with unmeasured confounding, (4) derived bounds for RDs, and (5) estimated the proportion of compliers and their characteristics. The findings revealed that IV assumptions were violated and RDs were extreme, but the assumptions became more plausible upon narrowing the calendar time range and restricting the cohort by excluding prevalent heart failure (the strongest measured predictor of outcome). Systematically evaluating IV assumptions could help detect bias in IV estimators and increase their validity. This article is part of a Special Collection on Pharmacoepidemiology.