Abstract
PurposeDefining 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.MethodsWe 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.ConclusionEvidence 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.
Highlights
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
We identified places where an investigator had to make a scientific decision between options or create study specific inputs to create an analytic dataset from raw longitudinal source data, including details of data source, inclusion/exclusion criteria, exposure definition, outcome definition, follow up, baseline covariates, as well as reporting on analysis methods
Our review identified many scientific decisions necessary to operate software solutions that would facilitate direct replication of an analytic cohort from raw source data captured in a longitudinal healthcare data source (Table 2)
Summary
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. In order to identify an initial list of key parameters that must be defined to implement a study, we reviewed 5 macro based programs and software systems designed to support healthcare database research (listed in appendix). We used this as a starting point because such programs are designed with flexible parameters to allow creation of customized study populations based on user specified scientific decisions.[54,57,58,59,60] These flexible parameters informed our catalogue of operational decisions that would have to be transparent for an independent investigator to fully understand how a study was implemented and be able to directly replicate a study. As we reviewed each tool, we added new parameters that had not been previously encountered and synonyms for different concepts
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