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

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

What is effective research communication? Towards cooperative inquiry with nunavut communities

Communication is recognized as the foundation of developing partnerships in science. In this study, we assess the effectiveness of several communication processes, practices, and tools used by wildlife researchers in northern communities in Arctic Canada. A case study was conducted in the communities of Cape Dorset and Coral Harbour (Salliq), Nunavut, Canada, to assess the effectiveness of research communication approaches carried out by the northern marine bird research group of Environment and Climate Change Canada, which has a long-standing research relationship with these two communities. Our objectives were to 1) explore local experiences with research—marine bird research in particular, 2) examine what communication approaches and tools Nunavummiut viewed as most effective for learning about research activities and feeling engaged in the process, and 3) identify new and emerging communication needs in Nunavut communities to support more effective research partnerships. Our findings indicate that several communication methods used by wildlife researchers, such as community meetings, have become less effective because of changing information-sharing practices at the community level. Other communication practices, such as using social media, hold much promise, but as of yet are underutilized by researchers, though of interest to northern communities. Acknowledging that every northern community is unique, with context-specific priorities, capacities, and needs, effective research partnerships should be built upon communication approaches that foster cooperative inquiry and learning. In progress towards this goal, we explore two emerging and related themes: first, access to information and communication technologies in the two communities, and second, the engagement of youth in Arctic research communication and delivery

The use of prior event rate ratio adjustment method for controlling unmeasured confounding in pharmacoepidemiologic studies: A cautionary note

Background: Unmeasured confounding is one of the principal problems in observational pharmacoepidemiologic studies. Prior event rate ratio (PERR) adjustment method has been proposed to control for unmeasured confounding. Objectives: To assess the performance of the PERR method in realistic pharmacoepidemiologic settings. Methods: Simulation studies were performed in several scenarios with varying effects of prior events on the probability of subsequent exposure, incidence rates, strength of confounders in prior and post periods, and rate of mortality/dropout. Exposure effects were estimated using conventional rate ratio (RR) and PERR adjustmentmethods. For the PERR method, the exposure effect is a ratio of two RRs: RR post exposure initiation and RR prior to initiation of exposure. In each simulation, the sample size was 100000 and each scenario was replicated 10000 times. 95% confidence intervals were estimated in a non-parametric way using the 2.5 and 97.5 percentiles of the 10000 estimates. Results: The exposure effects from the PERR adjustment method are highly biased when “prior” events influence the probability of subsequent exposure or when confounding differs considerably between prior and post periods. For example, the RR ranged from 1.52 to 1.10 (true RR= 2.00) when the effect of prior events on the exposure was RR 1.25 to 1.70, respectively. With a strong effect of prior events on the exposure (e.g. RR= 1.70), the bias of the estimates were more pronounced for PERR method than for the conventional method. In such case, even with a null exposure effect (RR = 1.00), the estimates shifted away from the null. In all settings, the confidence intervals of the estimates were wider for the PERR method than for the conventional method. Conclusions: The PERR adjustment method has significant limitations; in particular situations, e.g. when prior events strongly influence the probability of subsequent exposure, it can be more biased than conventional methods. Hence, caution should be exercised when applying this method and theoretical justification should be provided for underlying assumptions of the PERR

Evaluating different physician's prescribing preference based instrumental variables in the study of beta2-agonist use and the risk of acute myocardial infarction

Background: Instrumental variable (IV) analysis with physician's prescribing preference (PPP) as an IV has been used to control for unobserved confounding in pharmacoepidemiology. PPP can be defined in several ways, but it is unclear how different PPPs perform across databases. Objectives: To assess the validity of the IV PPP in two general practice (GP) databases in the study of inhaled long-acting beta2-agonist (LABA) use and the risk of acute myocardial infarction (AMI). Methods: Information on adult patients with a diagnosis of asthma and/or COPD and at least one prescription of an inhaled short-acting beta2-agonist (SABA)/LABA/ muscarinic antagonist (MA) was extracted from the British Clinical Practice Research Datalink (CPRD, n = 490499), and the Dutch Mondriaan (n = 27459) GP databases. Conventional Cox model and two-stage IV analysis were applied to estimate the effect of LABA vs. non-LABA (SABA/MA) on the risk of AMI. PPPs were defined by the proportion of LABA prescriptions per practice (PLP) or previous single (PPP1), or five (PPP5), or ten (PPP10) prescriptions by a physician. Quantitative methods (e.g. correlation (r), odds ratio (OR), standardized difference (SDif)) were used to assess the validity of the IVs. 95% confidence intervals (CI) for IV estimates were estimated using bootstrapping. Results: LABA was not associated with an increased risk of AMI, adjusted hazard ratio 0.96 [95%CI 0.89-1.02] (CPRD) and 1.18 [0.97-1.43] (Mondriaan) in conventional Cox model and 0.95 [0.55-1.63], 1.24 [0.40-3.60], and 1.24 [0.47-3.09] in IV analyses with PPP10 for CPRD, and PPP5 and PPP10 for Mondriaan, respectively. PLP, PPP1 and PPP5 in the CPRD and PPP1 in Mondriaan were weakly associated with LABA (r0.10) across PLP levels in Mondriaan. Conclusions: LABA use was not associated with an increased risk of AMI compared to non-LABA. Validity of IV depends on the definition of IV and the database in which it is applied. We recommend researchers to generate several possible IVs, assess their validity, and report the estimate(s) from the most valid IV

Application of the self-controlled case series design in pharmacoepidemiological studies: A cautionary note

Background: The self-controlled case-series (SCCS) design has been applied to control for time-fixed (un)measured confounding in pharmacoepidemiological studies. Although previous studies acknowledged that violations of the key SCCS assumptions lead to biased exposure effects, little is known about the impact of the violations in empirical studies. We aimed to evaluate the impact of various levels of violation of assumptions of the SCCS design and different definitions of observation/risk periods in a study of antidepressants use and risk of hip/femur fracture (HF). Methods: Information on adults with a hip/femur fracture (HF) who used antidepressants at any time during the observation period 2001-2009 was extracted from the UK THIN (6632 cases) and the Dutch Mondriaan (136 cases) databases. The incidence rate ratio (IRR) using this design was defined as the rate of events during exposed periods and during all other observed periods. The IRR of HF was estimated using conditional Poisson regression. Results: The IRRs appeared extremely biased when all subjects were censored at their first/last HF or when the analysis was restricted to subjects experiencing hip fracture after initiating antidepressant use. For example in THIN, IRRs for >365 days of exposure were 1.26 [1.13-1.42] when complete follow-up was considered and 40.1 [32.2-49.9] when censoring was at the first event. However, results were consistent when including subjects who were exposed at the start of follow-up and for different risk period definitions. Conclusion: The SCCS design is sensitive to violations of the assumptions and yields apparently biased estimates when a significant number of subjects is censored at the event or when the analysis is restricted subjects who experienced hip fracture after initiating antidepressants. The performance of this design may differ across studies and across databases. Therefore, in each SCCS study, correct specification of the SCCS design should be carefully assessed and reported