When questions of causality arise, epidemiologists widely acknowledge the randomized controlled trial (RCT) as the “gold standard” of research designs. In cases where RCTs are not possible–for financial, ethical, practi-cal, or other reasons–alternative methods must be used. These alternatives comprise the time-tested epidemiology toolbox: cohort studies, case-control studies, case-crossover studies, and their brethren. Although these approaches are appropriate in many instances, they do fundamentally lack an intervention; the classic experimental method of establishing causality is to intervene in one group while leaving a second control group aside. Nonexperimental methods of causal inference must rely on an assumption of no unmeasured confounding [1
], an assumption that is hard to justify in many cases, particularly in pharmacoe-pidemiologic studies based on health care claims and utilization data [3
For decades, economists have been using instrumental variable (IV) analysis as a method of causal inference in cases where an RCT is not possible and when an assumption of no unmeasured confounding is unwarranted (). Although IV analysis is certainly no panacea for all that ails the non-randomized study, it does offer a tool for instances when the alternative methods do not work.
Key points about IV analysis
As an example, consider a question in cardiac care: does catheterization prevent death after myocardial infarction (MI)? This question has been addressed in several IV studies by McClellan and Newhouse [6
In response, consider a group of patients who have experienced an MI. Divide these patients into two observed groups: those who were catheterized after their MI and those who were not; divide them again by who did and did not die. From the fabricated numbers in , an odds ratio of 0.211 and a risk difference (RD) of 0.150 can be calculated, indicating that catheterization is strongly associated with reduced risk of death. Causality, however, is unknown: the treatment may be highly protective, or selection into the catheterization group may be indicative of overall health and reduced risk of death. In this setting, covariates typically available in health care utilization data (prior MI, age, and history of various comorbid conditions), or even covariates frequently available in prospective cohort studies (smoking, body mass index, or blood pressure) are unlikely to be sufficient to control for confounding. If the decision to catheterize depends on these variables, the assumption of no unmeasured confounding cannot be justified.
Association between catheterization (X) and death (D) (crude exposure to outcome association: RD = 0.150)
What is new?
- Instrumental variable (IV) analysis provides a method to obtain a potentially unbiased estimate of treatment effect, even in the presence of strong unmeasured confounding.
- This article outlines the analytical method and the assumptions required for IV analysis.
- Several examples are provided to illustrate the strengths and potential pitfalls of the IV approach.
This article and its companion, “Instrumental variable application: In 25 variations, the physician prescribing preference generally was strong and reduced covariate imbalance,” together introduce the concept of IV analysis and examine some of the key assumptions un-derlying the technique. Taken together, the articles show how IVs arise in observational data and how IV analysis parallels randomized trial designs, and also examine the key notions of instrument strength and validity. Each of them describes instruments that have been used in clinical epidemiology and gives examples of IV analysis.
The problem of deducing causality is familiar to clinical epidemiologists; treatment-outcome relationships can be obscured by the combined effect of measured and unmea-sured confounders. Examples that may be affected include the use of hormone replacement therapy and incidence of coronary heart disease (CHD) [8
] and vitamin E supple-ments and CHD [9
]. In each of these cases, the nonrandom-ized results had been tantalizing, but their perceived unreliability [10
] prompted randomized trials to confirm or refute their findings [12
This article will introduce the use of IV analysis as a sup-plement to standard epidemiologic methods. It will explain IVs from a conceptual perspective by looking at how IV studies arise from their randomized trial analogs.