BACKGROUND: Researchers using observational data to understand drug effects must make a number of analytic design choices that suit the characteristics of the data and the subject of the study. Review of the published literature suggests that there is a lack of consistency even when addressing the same research question in the same database. OBJECTIVE: To characterize the degree of similarity or difference in the method and analysis choices made by observational database research experts when presented with research study scenarios. RESEARCH DESIGN: On-line survey using research scenarios on drug-effect studies to capture method selection and analysis choices that follow a dependency branching based on response to key questions. SUBJECTS: Voluntary participants experienced in epidemiological study design solicited for participation through registration on the Observational Medical Outcomes Partnership website, membership in particular professional organizations, or links in relevant newsletters. MEASURES: Description (proportion) of respondents selecting particular methods and making specific analysis choices based on individual drug-outcome scenario pairs. The number of questions/decisions differed based on stem questions of study design, time-at-risk, outcome definition, and comparator. RESULTS: There is little consistency across scenarios, by drug or by outcome of interest, in the decisions made for design and analyses in scenarios using large healthcare databases. The most consistent choice was the cohort study design but variability in the other critical decisions was common. CONCLUSIONS: There is great variation among epidemiologists in the design and analytical choices that they make when implementing analyses in observational healthcare databases. These findings confirm that it will be important to generate empiric evidence to inform these decisions and to promote a better understanding of the impact of standardization on research implementation.
BACKGROUND: Researchers using observational data to understand drug effects must make a number of analytic design choices that suit the characteristics of the data and the subject of the study. Review of the published literature suggests that there is a lack of consistency even when addressing the same research question in the same database. OBJECTIVE: To characterize the degree of similarity or difference in the method and analysis choices made by observational database research experts when presented with research study scenarios. RESEARCH DESIGN: On-line survey using research scenarios on drug-effect studies to capture method selection and analysis choices that follow a dependency branching based on response to key questions. SUBJECTS: Voluntary participants experienced in epidemiological study design solicited for participation through registration on the Observational Medical Outcomes Partnership website, membership in particular professional organizations, or links in relevant newsletters. MEASURES: Description (proportion) of respondents selecting particular methods and making specific analysis choices based on individual drug-outcome scenario pairs. The number of questions/decisions differed based on stem questions of study design, time-at-risk, outcome definition, and comparator. RESULTS: There is little consistency across scenarios, by drug or by outcome of interest, in the decisions made for design and analyses in scenarios using large healthcare databases. The most consistent choice was the cohort study design but variability in the other critical decisions was common. CONCLUSIONS: There is great variation among epidemiologists in the design and analytical choices that they make when implementing analyses in observational healthcare databases. These findings confirm that it will be important to generate empiric evidence to inform these decisions and to promote a better understanding of the impact of standardization on research implementation.
Authors: Marc A Suchard; Ivan Zorych; Shawn E Simpson; Martijn J Schuemie; Patrick B Ryan; David Madigan Journal: Drug Saf Date: 2013-10 Impact factor: 5.606
Authors: S Wessinger; M Kaplan; L Choi; M Williams; C Lau; L Sharp; M D Crowell; A Keshavarzian; M P Jones Journal: Aliment Pharmacol Ther Date: 2006-04-01 Impact factor: 8.171
Authors: Patrick B Ryan; Martijn J Schuemie; Emily Welebob; Jon Duke; Sarah Valentine; Abraham G Hartzema Journal: Drug Saf Date: 2013-10 Impact factor: 5.606