Inferring causality from observational studies: the role of instrumental variable analysis

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, because 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. This review presents such an approach, instrumental variable analysis, a technique that has been used by economists for many years but has only recently seen increasing use in the health care literature. This review provides 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 instrumental variable, and suggested elements for reporting an instrumental variable analysis are provided in the Supplementary Appendix.