A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records...

Area of Science:

• Clinical research methodology
• Health informatics
• Biostatistics

Background:

• Clinical trials are crucial for medical evidence but often lack population representativeness, impacting generalizability.
• Electronic Health Records (EHRs) offer a valuable data source for assessing clinical trial population representativeness.

Purpose of the Study:

• To systematically estimate clinical trial population representativeness using EHR data during the early design phase.
• To develop and apply an analytical framework for quantifying trial representativeness.

Main Methods:

• Developed an end-to-end framework to convert free-text clinical trial eligibility criteria into executable database queries.
• Utilized the Observational Medical Outcomes Partnership Common Data Model for query standardization.
• Quantified population representativeness for clinical trials using EHR data.

Main Results:

• Assessed 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the US.
• Found that 85.7% of COVID-19 trials and 30.1% of T2DM trials exhibited poor population representativeness.
• Identified overly restrictive eligibility criteria as a primary cause of poor representativeness.

Conclusions:

• EHR data can effectively assess clinical trial population representativeness.
• The developed framework provides data-driven metrics to guide the selection and optimization of clinical trial eligibility criteria.
• Improving representativeness enhances the generalizability of clinical trial findings.