Application of instrumental variables method in pharmacoepidemiology: An example of beta2-agonist use and myocardial infarction

Abstract

Background: Unobserved confounding may impair the validity of observational research. Instrumental variable (IV) analysis theoretically controls for unobserved confounding, yet it has not widely been used in pharmacoepidemiologic studies. Objectives: To assess the applicability and apparent validity of different IVs in a study of long-acting beta2-agonist (LABA) use and the risk of myocardial infarction (MI). Methods: Information on adult patients with a diagnosis of asthma and/or chronic obstructive pulmonary disease and at least one prescription of inhaled beta2- agonist/Muscarinic antagonist was extracted from Dutch Mondriaan NPCRD General Practice (GP) database (N = 360,000). Effects of LABA vs. no-LABA on the risk of MI were estimated by using a Cox proportional hazards model. Physician's prescribing preference (PPP), measured by the last prescription written by a physician, GP centers (GPC), and proportions of LABA prescriptions per GP center (PLP) were used as IVs in two-stage IV analysis. Ninety-five percent confidence intervals (CI) for IV estimates were estimated by using bootstrapping. Quantitative methods (e.g., F-statistic, standardized difference for binary IV, and empirical cumulative density function for continuous IV) were applied to assess the validity of the IVs. Results: IV analysis showed that GPC was weakly (F = 11) associated with LABA in contrast to the other IVs: PPP (F = 200) and PLP (F = 975). Observed confounders were approximately balanced across IV levels for PPP and PLP, but not for GPC. As this study has been performed under the PROTECT project examining the variability of results from studies using a same protocol, or a protocol with defined differences, applied to a same drug-adverse event pair in different databases, in order to maintain the blinding of investigators from one another's results, results on the association between LABA and MI will be disclosed during the ICPE conference. Conclusions: Our IV analysis suggests that PLP appears to perform better as an IV than PPP and GPC. We recommend researchers to start IV analysis with more than one possible IV in order to evade uncertainty of the effect estimate based on a single IV.