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

TLDR

Large healthcare encounter and reimbursement databases are increasingly used by decision‑makers, yet terminology varies and many decisions are required to define study populations and analytic datasets. The study aims to catalogue essential scientific decisions and agree on terminology that should be reported to enable replication and validity assessment of database studies. The authors reviewed key investigator decisions for creating analytic cohorts, consulted a panel of academic, regulatory, and industry experts, and identified minimum reporting elements—including operational definitions of temporal anchors, an attrition table, and a design diagram. Greater transparency about operational study parameters could substantially improve reproducibility, rigor, and confidence in real‑world evidence from healthcare databases.

Abstract

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.