Title: Preface to HSOR special issue on instrumental variable methods
Abstract: Selection bias is a central issue for health services and outcomes research. In observational studies, individuals who receive a treatment often differ from those who do not. Even in randomized controlled trials, those who comply with the protocol often differ from those who do not. Within the field of causal inference, there are methods for adjusting for measured confounders; however, remaining bias due to unmeasured confounders and model misspecification is always a concern. About 10 years ago, Health Services & Outcomes Research Methodology published a special issue on Causal Inference that was well-received in the field (Ash et al. 2001). The current special issue builds upon the previous special issue, with a focus on Instrumental Variable (IV) method, an approach for making causal inference about the effect of a treatment, even when there is unmeasured confounding. This is particularly attractive, as other causal methods such as propensity score matching can adjust only for observed characteristics and it is generally difficult to test whether hidden bias exists. However, the validity of the IV approach relies upon a set of strong assumptions that may not hold perfectly in health services and outcomes research. Thus, the benefit of IV’s ability to remove unmeasured confounding may be lost, at least in part, when these strong assumptions fail to hold. The four articles in this special issue (Baiocchi et al. 2012; Lu and Marcus 2012; Marcus et al. 2012; O’Malley 2012) focus on clarifying the IV assumptions and how to strengthen the causal inference when the IV assumptions fail to hold perfectly, as is often the case in ‘real life’ health services and outcomes research context. O’Malley (2012), examine several longitudinal models and specifications of IV models with respect to underlying assumptions and resulting empirical consequences. The focus is on whether longitudinal data and assumptions can strengthen the IV inference. These methods are illustrated with data from a longitudinal study of mental health costs related to newer and more expensive second generation antipsychotics. Baiocchi et al. (2012) present an innovative study design approach to IV called near/far matching. This approach is similar to propensity score matching; however, it can estimate causal effects when there is unmeasured confounding. Near/far matching is applied to