Literature DB >> 32250527

Transparency in real-world evidence (RWE) studies to build confidence for decision-making: Reporting RWE research in diabetes.

Elisabetta Patorno1, Sebastian Schneeweiss1, Shirley V Wang1.   

Abstract

Transparency of real-world evidence (RWE) studies is critical to understanding how findings of a specific study were derived and is a necessary foundation to assessing validity and determination of whether decisions should be informed by the findings. In the present paper, we lay out strategies to improve clarity in the reporting of comparative effectiveness studies using real-world data that were generated by the routine operation of a healthcare system. This may include claims data, electronic health records, wearable devices, patient-reported outcomes or patient registries. These recommendations were discussed with multiple stakeholders, including regulators, payers, academics and journal editors, and endorsed by two professional societies that focus on RWE. We remind readers interested in diabetes research of the utility of conceptualizing a target trial that is then emulated by a RWE study when planning and communicating about RWE study implementation. We recommend the use of a graphical representation showcasing temporality of key longitudinal study design choices. We highlight study elements that should be reported to provide the clarity necessary to make a study reproducible. Finally, we suggest registering study protocols to increase process transparency. With these tools the readership of diabetes RWE studies will be able to more efficiently understand each study and be more able to assess a study's validity with reasonably high confidence before making decisions based on its findings.
© 2019 John Wiley & Sons Ltd.

Entities:  

Keywords:  cohort study; pharmaco-epidemiology; population study; type 2 diabetes

Mesh:

Year:  2020        PMID: 32250527      PMCID: PMC7472869          DOI: 10.1111/dom.13918

Source DB:  PubMed          Journal:  Diabetes Obes Metab        ISSN: 1462-8902            Impact factor:   6.577


  50 in total

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4.  Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

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Journal:  Am J Epidemiol       Date:  2016-03-18       Impact factor: 4.897

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Journal:  Drug Saf       Date:  2017-10       Impact factor: 5.606

6.  A prospective, observational study of postmenopausal hormone therapy and primary prevention of cardiovascular disease.

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7.  Inhibitors of hydroxymethylglutaryl-coenzyme A reductase and risk of fracture among older women.

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8. 

Authors:  Sinéad M Langan; Sigrún A J Schmidt; Kevin Wing; Vera Ehrenstein; Stuart G Nicholls; Kristian B Filion; Olaf Klungel; Irene Petersen; Henrik T Sørensen; William G Dixon; Astrid Guttmann; Katie Harron; Lars G Hemkens; David Moher; Sebastian Schneeweiss; Liam Smeeth; Miriam Sturkenboom; Erik von Elm; Shirley V Wang; Eric I Benchimol
Journal:  CMAJ       Date:  2019-06-24       Impact factor: 8.262

9.  Reanalysis of two studies with contrasting results on the association between statin use and fracture risk: the General Practice Research Database.

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10.  Use of sodium glucose cotransporter 2 inhibitors and risk of major cardiovascular events and heart failure: Scandinavian register based cohort study.

Authors:  Björn Pasternak; Peter Ueda; Björn Eliasson; Ann-Marie Svensson; Stefan Franzén; Soffia Gudbjörnsdottir; Kristian Hveem; Christian Jonasson; Viktor Wintzell; Mads Melbye; Henrik Svanström
Journal:  BMJ       Date:  2019-08-29
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  2 in total

1.  Rules Based Data Quality Assessment on Claims Database.

Authors:  Mary A Gadde; Zhan Wang; Meredith Zozus; John B Talburt; Melody L Greer
Journal:  Stud Health Technol Inform       Date:  2020-06-26

2.  Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments.

Authors:  Sebastian Schneeweiss; Elisabetta Patorno
Journal:  Endocr Rev       Date:  2021-09-28       Impact factor: 19.871

  2 in total

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