Instrumental variable methods in comparative safety and effectiveness research

Instrumental variable (IV) methods have been proposed as a potential approach to the common problem of uncontrolled confounding in comparative studies of medical interventions, but IV methods are unfamiliar to many researchers. The goal of this article is to provide a non-technical, practical introduction to IV methods for comparative safety and effectiveness research. We outline the principles and basic assumptions necessary for valid IV estimation, discuss how to interpret the results of an IV study, provide a review of instruments that have been used in comparative effectiveness research, and suggest some minimal reporting standards for an IV analysis. Finally, we offer our perspective of the role of IV estimation vis-à-vis more traditional approaches based on statistical modeling of the exposure or outcome. We anticipate that IV methods will be often underpowered for drug safety studies of very rare outcomes, but may be potentially useful in studies of intended effects where uncontrolled confounding may be substantial

Active safety monitoring of newly marketed medications in a distributed data network: application of a semi-automated monitoring system

We developed a semi-automated active monitoring system that uses sequential matched-cohort analyses to assess drug safety across a distributed network of longitudinal electronic healthcare data. In a retrospective analysis, we showed that the system would have identified cerivastatin-induced rhabdomyolysis. In this study, we evaluated whether the system would generate alerts for three drug-outcome pairs: rosuvastatin and rhabdomyolysis (known null association), rosuvastatin and diabetes mellitus, and telithromycin and hepatotoxicity (two examples for which alerting would be questionable). During >5 years of monitoring, rate differences (RDs) comparing rosuvastatin to atorvastatin were -0.1 cases of rhabdomyolysis per 1,000 person-years (95% CI, -0.4, 0.1) and -2.2 diabetes cases per 1,000 person-years (95% CI, -6.0, 1.6). The RD for hepatotoxicity comparing telithromycin to azithromycin was 0.3 cases per 1,000 person-years (95% CI, -0.5, 1.0). In a setting in which false positivity is a major concern, the system did not generate alerts for three drug-outcome pairs

Type of stress ulcer prophylaxis and risk of nosocomial pneumonia in cardiac surgical patients: cohort study

Objective: To examine the relation between the type of stress ulcer prophylaxis administered and the risk of postoperative pneumonia in patients undergoing coronary artery bypass grafting. Design: Retrospective cohort study. Setting: Premier Research Database. Participants:: 21 214 patients undergoing coronary artery bypass graft surgery between 2004 and 2010; 9830 (46.3%) started proton pump inhibitors and 11 384 (53.7%) started H2 receptor antagonists in the immediate postoperative period. Main outcome measure Occurrence of postoperative pneumonia, assessed using appropriate diagnostic codes. Results: Overall, 492 (5.0%) of the 9830 patients receiving a proton pump inhibitor and 487 (4.3%) of the 11 384 patients receiving an H2 receptor antagonist developed postoperative pneumonia during the index hospital admission. After propensity score adjustment, an elevated risk of pneumonia associated with treatment with proton pump inhibitors compared with H2 receptor antagonists remained (relative risk 1.19, 95% confidence interval 1.03 to 1.38). In the instrumental variable analysis, use of a proton pump inhibitor (compared with an H2 receptor antagonist) was associated with an increased risk of pneumonia of 8.2 (95% confidence interval 0.5 to 15.9) cases per 1000 patients. Conclusions: Patients treated with proton pump inhibitors for stress ulcer had a small increase in the risk of postoperative pneumonia compared with patients treated with H2 receptor antagonists; this risk remained after confounding was accounted for using multiple analytic approaches

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0.

PURPOSE: Defining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases. METHODS: We reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list. CONCLUSION: Evidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision-makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases

Covariate Selection in High-Dimensional Propensity Score Analyses of Treatment Effects in Small Samples

To reduce bias by residual confounding in nonrandomized database studies, the high-dimensional propensity score (hd-PS) algorithm selects and adjusts for previously unmeasured confounders. The authors evaluated whether hd-PS maintains its capabilities in small cohorts that have few exposed patients or few outcome events. In 4 North American pharmacoepidemiologic cohort studies between 1995 and 2005, the authors repeatedly sampled the data to yield increasingly smaller cohorts. They identified potential confounders in each sample and estimated both an hd-PS that included 0–500 covariates and treatment effects adjusted by decile of hd-PS. For sensitivity analyses, they altered the variable selection process to use zero-cell correction and, separately, to use only the variables’ exposure association. With >50 exposed patients with an outcome event, hd-PS-adjusted point estimates in the small cohorts were similar to the full-cohort values. With 25–50 exposed events, both sensitivity analyses yielded estimates closer to those obtained in the full data set. Point estimates generally did not change as compared with the full data set when selecting >300 covariates for the hd-PS. In these data, using zero-cell correction or exposure-based covariate selection allowed hd-PS to function robustly with few events. hd-PS is a flexible analytical tool for nonrandomized research across a range of study sizes and event frequencies

Graphical Depiction of Longitudinal Study Designs in Health Care Databases

Pharmacoepidemiologic and pharmacoeconomic analysis of health care databases has become a vital source of evidence to support health care decision making and efficient management of health care organizations. However, decision makers often consider studies done in nonrandomized health care databases more difficult to review than randomized trials because many design choices need to be considered. This is perceived as an important barrier to decision making about the effectiveness and safety of medical products. Design flaws in longitudinal database studies are avoidable but can be unintentionally obscured in the convoluted prose of methods sections, which often lack specificity. We propose a simple framework of graphical representation that visualizes study design implementations in a comprehensive, unambiguous, and intuitive way; contains a level of detail that enables reproduction of key study design variables; and uses standardized structure and terminology to simplify review and communication to a broad audience of decision makers. Visualization of design details will make database studies more reproducible, quicker to review, and easier to communicate to a broad audience of decision makers

Heart failure risk among patients with rheumatoid arthritis starting a TNF antagonist

BACKGROUND: While heart failure (HF) is associated with elevations in tumor necrosis factor (TNF)alpha, several trials of TNF antagonists showed no benefit and possibly worsening of disease in those with known severe HF. We studied the risk of new or recurrent HF among a group of patients receiving these agents to treat rheumatoid arthritis (RA). METHODS: We used data from four different US healthcare programmes. Subjects with RA receiving methotrexate were eligible to enter the study cohort if they added or switched to a TNF antagonist or another non-biological disease modifying antirheumatic drug (nbDMARD). These groups were compared in Cox regression models stratified by propensity score decile and adjusted for oral glucocorticoid dosage, prior HF hospitalisations, and the use of loop diuretics. RESULTS: We compared 8656 new users of a nbDMARD with 11 587 new users of a TNF antagonist with similar baseline covariates. The HR for the TNF antagonists compared with nbDMARD was 0.85 (95% CI 0.63 to 1.14). The HR was also not elevated in subjects with a history of HF. But, it was elevated prior to 2002 (HR 2.17, 95% CI 0.45 to 10.50, test for interaction p=0.036). Oral glucocorticoids were associated with a dose-related gradient of HF risk: compared with no use, 1/=5 mg HR 1.54 (95% CI 1.09 to 2.19). CONCLUSIONS: TNF antagonists were not associated with a risk of HF hospital admissions compared with nbDMARDs in this RA population