Ryan M Carnahan1, Joshua J Gagne2, Christian Hampp3, Charles E Leonard4, Sengwee Toh5, Candace C Fuller5, Sean Hennessy4, Laura Hou5, Noelle M Cocoros5, Genna Panucci5, Tiffany Woodworth5, Austin Cosgrove5, Aarthi Iyer5, Elizabeth A Chrischilles6. 1. Department of Epidemiology, College of Public Health, University of Iowa, 145 N. Riverside Dr., S437 CPHB, Iowa City, IA, 52242, USA. Ryan-Carnahan@uiowa.edu. 2. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. 3. Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA. 4. Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, and Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. 5. Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA. 6. Department of Epidemiology, College of Public Health, University of Iowa, 145 N. Riverside Dr., S437 CPHB, Iowa City, IA, 52242, USA.
Abstract
BACKGROUND: The US Food and Drug Administration's Sentinel System was established to monitor safety of regulated medical products. Sentinel investigators identified known associations between drugs and adverse events to test reusable analytic tools developed for Sentinel. This test case used a comparator with a different indication. OBJECTIVE: We tested the ability of Sentinel's reusable analytic tools to identify the known association between warfarin and gastrointestinal bleeding (GIB). Statins, expected to have no effect on GIB, were the comparator. We further explored the impact of analytic features, including matching ratio and stratifying Cox regression analyses, on matched pairs. METHODS: This evaluation included data from 14 Sentinel Data Partners. New users of warfarin and statins, aged 18 years and older, who had not received other anticoagulants or had recent GIB were matched on propensity score using 1:1 and 1:n variable ratio matching, matching statin users with warfarin users to estimate the average treatment effect in warfarin-treated patients. We compared the risk of GIB using Cox proportional hazards regression, following patients for the duration of their observed continuous treatment or until a GIB. For the 1:1 matched cohort, we conducted analyses with and without stratification on matched pair. The variable ratio matched cohort analysis was stratified on the matched set. RESULTS: We identified 141,398 new users of warfarin and 2,275,694 new users of statins. In analyses stratified on matched pair/set, the hazard ratios (HR) for GIB in warfarin users compared with statin users were 2.78 (95% confidence interval [CI] 2.36-3.28) in the 1:1 matched cohort and 3.10 (95% CI 2.76-3.49) in the variable ratio matched cohort. The HR was lower in the analysis of the 1:1 matched cohort not stratified by matched pair (2.22, 95% CI 1.97-2.49), and highest early in treatment. Follow-up for warfarin users tended to be shorter than for statin users. CONCLUSIONS: This study identified the expected GIB risk with warfarin compared with statins using an analytic tool developed for Sentinel. Our findings suggest that comparators with different indications may be useful in surveillance in select circumstances. Finally, in the presence of differential censoring, stratification by matched pair may reduce the potential for bias in Cox regression analyses.
BACKGROUND: The US Food and Drug Administration's Sentinel System was established to monitor safety of regulated medical products. Sentinel investigators identified known associations between drugs and adverse events to test reusable analytic tools developed for Sentinel. This test case used a comparator with a different indication. OBJECTIVE: We tested the ability of Sentinel's reusable analytic tools to identify the known association between warfarin and gastrointestinal bleeding (GIB). Statins, expected to have no effect on GIB, were the comparator. We further explored the impact of analytic features, including matching ratio and stratifying Cox regression analyses, on matched pairs. METHODS: This evaluation included data from 14 Sentinel Data Partners. New users of warfarin and statins, aged 18 years and older, who had not received other anticoagulants or had recent GIB were matched on propensity score using 1:1 and 1:n variable ratio matching, matching statin users with warfarin users to estimate the average treatment effect in warfarin-treated patients. We compared the risk of GIB using Cox proportional hazards regression, following patients for the duration of their observed continuous treatment or until a GIB. For the 1:1 matched cohort, we conducted analyses with and without stratification on matched pair. The variable ratio matched cohort analysis was stratified on the matched set. RESULTS: We identified 141,398 new users of warfarin and 2,275,694 new users of statins. In analyses stratified on matched pair/set, the hazard ratios (HR) for GIB in warfarin users compared with statin users were 2.78 (95% confidence interval [CI] 2.36-3.28) in the 1:1 matched cohort and 3.10 (95% CI 2.76-3.49) in the variable ratio matched cohort. The HR was lower in the analysis of the 1:1 matched cohort not stratified by matched pair (2.22, 95% CI 1.97-2.49), and highest early in treatment. Follow-up for warfarin users tended to be shorter than for statin users. CONCLUSIONS: This study identified the expected GIB risk with warfarin compared with statins using an analytic tool developed for Sentinel. Our findings suggest that comparators with different indications may be useful in surveillance in select circumstances. Finally, in the presence of differential censoring, stratification by matched pair may reduce the potential for bias in Cox regression analyses.
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