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Review
. 2020 Apr;107(4):827-833.
doi: 10.1002/cpt.1577. Epub 2019 Aug 25.

Choosing Among Common Data Models for Real-World Data Analyses Fit for Making Decisions About the Effectiveness of Medical Products

Sebastian Schneeweiss  1 , Jeff S Brown  2 , Andrew Bate  3 , Gianluca Trifirò  4 , Dorothee B Bartels  5
Affiliations
Review

Choosing Among Common Data Models for Real-World Data Analyses Fit for Making Decisions About the Effectiveness of Medical Products

Sebastian Schneeweiss et al. Clin Pharmacol Ther. 2020 Apr.

Abstract

Many real-world data analyses use common data models (CDMs) to standardize terminologies for medication use, medical events and procedures, data structures, and interpretations of data to facilitate analyses across data sources. For decision makers, key aspects that influence the choice of a CDM may include (i) adaptability to a specific question; (ii) transparency to reproduce findings, assess validity, and instill confidence in findings; and (iii) ease and speed of use. Organizing CDMs preserve the original information from a data source and have maximum adaptability. Full mapping data models, or preconfigured rules systems, are easy to use, since all raw codes are mapped to medical constructs. Adaptive rule systems grow libraries of reusable measures that can easily adjust to preserve adaptability, expedite analyses, and ensure study-specific transparency.

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