Instrumental variable analysis of secondary pharmacoepidemiologic data.

Epidemiologic studies of the effects of drugs are essential. Increasingly, these studies are based on large administra tive data files containing longitudinal drug exposure and clinical outcome data for entire populations. These data allow for rapid, retrospective evaluation of the intended and adverse effects of prescription medications as they are used in routine practice. Health care claims data, however, do not contain measurements of many important risk factors that physicians use to make prescribing decisions. Confounding by unmea sured indication is a threat to the validity of most pharmaco epidemiologic studies but is likely to be particularly severe for studies based on administrative data.' Instrumental variable (IV) methods present a possible solution to the problem of residual confounding, provided suitable instruments can be identified. For an IV to reduce confounding in the initial treatment assignment, it should influence the prescribing decision but have no direct effect on the health outcome under study. We have proposed an instru ment related to physician prescribing preference for use in database studies of short-term drug effects.2 In this issue of EPIDEMIOLOGY, Hernan and Robins3 give a comprehensive overview of IV methods and provide a detailed commentary on our analysis. We would like to review briefly the central assumptions of our study, discuss an additional concern raised by Hernan and Robins, and elaborate on further aspects of this analysis and the IV method in the context of empirical pharmacoepidemiologic research with secondary data. In our study, we assessed the risk of gastrointestinal (GI) toxicity associated with selective versus nonselective nonsteroidal anti-inflammatory drug (NSAID) exposure using a physician's NSAID preference (as measured by the last NSAID prescription written by a physician) as an instrument. When treatment effects are homogeneous, the internal valid ity of our approach relies principally on 3 assumptions: (1) physicians differ in their NSAID preference; (2) a physician's NSAID prescribing preference as reflected in the last NSAID prescription he has written is unrelated to unmeasured risk factors in his current patient; and (3) a physician's NSAID prescribing preference as reflected in the last prescription written has no direct effect on the outcome in the current patient (known as the exclusion restriction). As stated,2 these assumptions are not verifiable with data, but their plausibility can be evaluated in a variety of ways. Hernan and Robins3 raise the important point that if the effect of treatment is heterogeneous (eg, if selective NSAIDs are protective in some patients and hazardous in others), then an additional assumption is required to justify the use of the usual IV estimator. One such assumption states that there should be no additive effect modification by the instrument among the treated patients (and similarly among the untreated patients). Although the possibility of effect modification by the instrument seems remote, Hema'n and Robins make the interesting observation that this assumption can be violated if the instrument interacts with important unmeasured GI risk factors (eg, tobacco use, alcohol consumption, obesity) to determine treatment. For example, this could happen if phy sicians who prefer nonselective NSAIDs rely strongly on a patient's smoking status to make treatment decisions, whereas selective NSAID preferring physicians decide on treatment without much consideration of smoking status. Such interactions could also lead to a violation of the "mono tonicity" assumption of Imbens and Angrist4 5-an alterna tive assumption that can be used to justify the use of the usual IV estimator when there is suspected treatment effect heter ogeneity. The assumption that the instrument does not interact with unmeasured GI risk factors to determine treatment is not empirically testable. However, its plausibility can be partly evaluated by exploring whether or not the instrument inter acts with measured GI risk factors to determine treatment. In our study, there were no such interactions, leading us to suspect that strong interactions with the unmeasured GI risk factors are unlikely. Although we do not think that treatment effect heterogeneity is a first-order source of bias in our study of NSAIDs, the issue is subtle and deserves further research. Our intention-to-treat approach has intrinsic limitations independent of the issues of IVs. Because many patients who initiate NSAIDs discontinue them by 60 days, the effect of treatment assignment might be different from the effect of uninterupted exposure. Furthermore, discontinuation may be more likely for nonselective NSAID users, leading to a bias against selective NSAIDs. We reduced this bias by focusing on short-term effects. Alternative "as-treated" longitudinal ap proaches suffer from confounding by informative treatment changes that occur during follow-up (for example, unrecorded GI symptoms that lead to the discontinuation of nonselective NSAIDs). The approach we adopted estimates the short-term effect of a treatment assignment that occurred in a routine care From the Division of Pharmacoepidemiology, Harvard Medical School, Brigham and Women's Hospital, Boston, MA. Correspondence: M. Alan Brookhart, Harvard Medical School, Brigham and Women's Hospital, 1620 Tremont St., Suite 3030, Boston, MA 02120. E-mail: abrookhart@rics.bwh.harvard.edu Copyright C 2006 by Lippincott Williams & Wilkins ISSN: 1044-3983/06/1704-0373 DOI: 10.1097/01.ede.0000222026.42077.ee