Published Sep 2017
Wang SV, Schneeweiss S, Berger ML. Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0. Value Health 2017; (8):1009-1022.
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
Keywords: Transparency, reproducibility, replication, healthcare databases, pharmacoepidemiology, methods, longitudinal data.
Copyright © 2017, International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc.