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

Shirley V. Wang*, Sebastian Schneeweiss, Marc L. Berger, Jeffrey Brown, Frank de Vries, Ian Douglas, Joshua J. Gagne, Rosa Gini, Olaf Klungel, C. Daniel Mullins, Michael D. Nguyen, Jeremy A. Rassen, Liam Smeeth, Miriam Sturkenboom, joint ISPE-ISPOR Special Task

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

48 Citations (Web of Science)

Abstract

Purpose: Defining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases. Methods: We reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list. Conclusion: 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.

Original languageEnglish
Pages (from-to)1009-1022
Number of pages14
JournalValue in Health
Volume20
Issue number8
DOIs
Publication statusPublished - Sep 2017

Keywords

  • Transparency
  • reproducibility
  • replication
  • healthcare databases
  • pharmacoepidemiology
  • methods
  • longitudinal data
  • SAFETY SURVEILLANCE
  • DECISION-MAKING
  • CLINICAL-TRIALS
  • PHARMACOEPIDEMIOLOGY
  • RISK
  • DRUG
  • GUIDELINES
  • PCORNET
  • DESIGN
  • COHORT

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