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. 2017 Sep;26(9):1018-1032.
doi: 10.1002/pds.4295.

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

Shirley V Wang  1   2 , Sebastian Schneeweiss  1   2 , Marc L Berger  3 , Jeffrey Brown  4 , Frank de Vries  5 , Ian Douglas  6 , Joshua J Gagne  1   2 , Rosa Gini  7 , Olaf Klungel  8 , C Daniel Mullins  9 , Michael D Nguyen  10 , Jeremy A Rassen  11 , Liam Smeeth  6 , Miriam Sturkenboom  12 ; joint ISPE-ISPOR Special Task Force on Real World Evidence in Health Care Decision Making
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Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

Shirley V Wang et al. Pharmacoepidemiol Drug Saf. 2017 Sep.

Erratum in

  • Erratum.
    [No authors listed] [No authors listed] Pharmacoepidemiol Drug Saf. 2017 Dec;26(12):1570. doi: 10.1002/pds.4353. Pharmacoepidemiol Drug Saf. 2017. PMID: 29205684 Free PMC article. No abstract available.

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.

Keywords: Transparency; healthcare databases; longitudinal data; methods; pharmacoepidemiology; replication; reproducibility.

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Figures

Figure 1
Figure 1
Data provenance: transitions from healthcare delivery to analysis results. [Colour figure can be viewed at wileyonlinelibrary.com]
Figure 2
Figure 2
Example design diagram. [Colour figure can be viewed at wileyonlinelibrary.com]
See this image and copyright information in PMC

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