LAUSR

Inferring causality from observational studies can be challenging because of the perennial threat of biases from selection, measurement, and confounding. The gold standard study design in clinical research is the randomized controlled trial, since random allocation to treatment ensures that, on average, comparison groups are balanced with respect to both known and unknown prognostic factors. However, most clinically relevant exposure-outcome relationships are not amendable (logistically or ethically) to randomization. Thus, there has been an emergence of analytical approaches over the last several years to improve the validity of inferences made from observational studies. We present herein one such approach, instrumental variable (IV) analysis, a technique that has been used by economists for many years but has only recently seen increasing use in the health care literature. We provide a description of the method, the assumptions underlying it, and recent applications in nephrology outcomes research. A more detailed review of the underlying mathematics, properties of an IV, and suggested elements for reporting an IV analysis are provided in the Supplementary Appendix.