David Madigan1, Patrick B Ryan2, Martijn Schuemie3. 1. Professor and Chair, Department of Statistics, Columbia University, 1255 Amsterdam Ave., New York, NY 10027, USA. 2. Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, MD and Janssen Research and Development LLC, Titusville, NJ, USA. 3. Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, MD and Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.
Abstract
BACKGROUND: Clinical studies that use observational databases, such as administrative claims and electronic health records, to evaluate the effects of medical products have become commonplace. These studies begin by selecting a particular study design, such as a case control, cohort, or self-controlled design, and different authors can and do choose different designs for the same clinical question. Furthermore, published papers invariably report the study design but do not discuss the rationale for the specific choice. Studies of the same clinical question with different designs, however, can generate different results, sometimes with strikingly different implications. Even within a specific study design, authors make many different analytic choices and these too can profoundly impact results. In this paper, we systematically study heterogeneity due to the type of study design and due to analytic choices within study design. METHODS AND FINDINGS: We conducted our analysis in 10 observational healthcare databases but mostly present our results in the context of the GE Centricity EMR database, an electronic health record database containing data for 11.2 million lives. We considered the impact of three different study design choices on estimates of associations between bisphosphonates and four particular health outcomes for which there is no evidence of an association. We show that applying alternative study designs can yield discrepant results, in terms of direction and significance of association. We also highlight that while traditional univariate sensitivity analysis may not show substantial variation, systematic assessment of all analytical choices within a study design can yield inconsistent results ranging from statistically significant decreased risk to statistically significant increased risk. Our findings show that clinical studies using observational databases can be sensitive both to study design choices and to specific analytic choices within study design. CONCLUSION: More attention is needed to consider how design choices may be impacting results and, when possible, investigators should examine a wide array of possible choices to confirm that significant findings are consistently identified.
BACKGROUND: Clinical studies that use observational databases, such as administrative claims and electronic health records, to evaluate the effects of medical products have become commonplace. These studies begin by selecting a particular study design, such as a case control, cohort, or self-controlled design, and different authors can and do choose different designs for the same clinical question. Furthermore, published papers invariably report the study design but do not discuss the rationale for the specific choice. Studies of the same clinical question with different designs, however, can generate different results, sometimes with strikingly different implications. Even within a specific study design, authors make many different analytic choices and these too can profoundly impact results. In this paper, we systematically study heterogeneity due to the type of study design and due to analytic choices within study design. METHODS AND FINDINGS: We conducted our analysis in 10 observational healthcare databases but mostly present our results in the context of the GE Centricity EMR database, an electronic health record database containing data for 11.2 million lives. We considered the impact of three different study design choices on estimates of associations between bisphosphonates and four particular health outcomes for which there is no evidence of an association. We show that applying alternative study designs can yield discrepant results, in terms of direction and significance of association. We also highlight that while traditional univariate sensitivity analysis may not show substantial variation, systematic assessment of all analytical choices within a study design can yield inconsistent results ranging from statistically significant decreased risk to statistically significant increased risk. Our findings show that clinical studies using observational databases can be sensitive both to study design choices and to specific analytic choices within study design. CONCLUSION: More attention is needed to consider how design choices may be impacting results and, when possible, investigators should examine a wide array of possible choices to confirm that significant findings are consistently identified.
Entities:
Keywords:
analysis; health outcomes; healthcare database; study design
Authors: Joshua J Gagne; Bruce Fireman; Patrick B Ryan; Malcolm Maclure; Tobias Gerhard; Sengwee Toh; Jeremy A Rassen; Jennifer C Nelson; Sebastian Schneeweiss Journal: Pharmacoepidemiol Drug Saf Date: 2012-01 Impact factor: 2.890
Authors: Albert G Crawford; Christine Cote; Joseph Couto; Mehmet Daskiran; Candace Gunnarsson; Kara Haas; Sara Haas; Somesh C Nigam; Rob Schuette; Joseph Yaskin Journal: Popul Health Manag Date: 2010-06 Impact factor: 2.459
Authors: Paul E Stang; Patrick B Ryan; J Marc Overhage; Martijn J Schuemie; Abraham G Hartzema; Emily Welebob Journal: Drug Saf Date: 2013-10 Impact factor: 5.606
Authors: Stylianos Serghiou; Chirag J Patel; Yan Yu Tan; Peter Koay; John P A Ioannidis Journal: J Clin Epidemiol Date: 2015-09-28 Impact factor: 6.437
Authors: Erica A Voss; Rupa Makadia; Amy Matcho; Qianli Ma; Chris Knoll; Martijn Schuemie; Frank J DeFalco; Ajit Londhe; Vivienne Zhu; Patrick B Ryan Journal: J Am Med Inform Assoc Date: 2015-02-10 Impact factor: 4.497
Authors: Connor A Emdin; Allan J Hsiao; Amit Kiran; Nathalie Conrad; Gholamreza Salimi-Khorshidi; Mark Woodward; Simon G Anderson; Hamid Mohseni; John J V McMurray; John G F Cleland; Henry Dargie; Suzanna Hardman; Theresa McDonagh; Kazem Rahimi Journal: Am J Cardiol Date: 2016-11-01 Impact factor: 2.778
Authors: Lawrence Bai; Madeleine K D Scott; Ethan Steinberg; Laurynas Kalesinskas; Aida Habtezion; Nigam H Shah; Purvesh Khatri Journal: J Am Med Inform Assoc Date: 2021-10-12 Impact factor: 4.497
Authors: M Lavallee; T Yu; L Evans; M Van Hemelrijck; C Bosco; A Golozar; A Asiimwe Journal: BMC Med Inform Decis Mak Date: 2022-02-03 Impact factor: 2.796