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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 3 of 3 Research Studies DisplayedFitzHenry F, Resnic FS, Robbins SL
Creating a common data model for comparative effectiveness with the observational medical outcomes partnership.
This case study describes the challenges and opportunities of a study specific use of the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) by two health systems and describe three comparative effectiveness use cases developed from the CDM. It concluded that the data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data.
AHRQ-funded; HS019913.
Citation: FitzHenry F, Resnic FS, Robbins SL .
Creating a common data model for comparative effectiveness with the observational medical outcomes partnership.
Appl Clin Inform 2015 Aug 26;6(3):536-47. doi: 10.4338/aci-2014-12-cr-0121..
Keywords: Comparative Effectiveness, Outcomes, Data
Li T, Vedula SS, Hadar N
Innovations in data collection, management, and archiving for systematic reviews.
The authors provide a step-by-step tutorial for collecting, managing, and archiving data for systematic reviews and suggest steps for developing rigorous data collection forms in the Systematic Review Data Repository to facilitate implementation of the methodological standards and expectations of the Institute of Medicine and other organizations.
AHRQ-funded; 290200710055I; 290201200012I.
Citation: Li T, Vedula SS, Hadar N .
Innovations in data collection, management, and archiving for systematic reviews.
Ann Intern Med. 2015 Feb 17;162(4):287-94. doi: 10.7326/M14-1603..
Keywords: Data, Comparative Effectiveness, Outcomes, Research Methodologies
Schmid CH, Trikalinos Olkin, I
Bayesian network meta-analysis for unordered categorical outcomes with incomplete data.
The researchers developed a Bayesian multinomial network meta-analysis model for unordered (nominal) categorical outcomes that allows for partially observed data in which exact event counts may not be known for each category. Their model properly accounts for correlations of counts in mutually exclusive categories and enables proper comparison and ranking of treatment effects across multiple treatments and multiple outcomes categories.
AHRQ-funded; HS018574.
Citation: Schmid CH, Trikalinos Olkin, I .
Bayesian network meta-analysis for unordered categorical outcomes with incomplete data.
Res Synth Methods 2014 Jun;5(2):162-85. doi: 10.1002/jrsm.1103..
Keywords: Comparative Effectiveness, Outcomes, Data