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Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0.

Shirley V WangSebastian SchneeweissMarc L BergerJeffrey BrownFrank de VriesIan DouglasJoshua J GagneRosa GiniOlaf KlungelC Daniel MullinsMichael D NguyenJeremy A RassenLiam SmeethMiriam Sturkenboomnull null
Published in: Pharmacoepidemiology and drug safety (2018)
Evidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision-makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.
Keyphrases
  • healthcare
  • adverse drug
  • emergency department
  • health information
  • machine learning
  • cross sectional
  • social media
  • electronic health record