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Generalizability in real-world trials
Anatol-Fiete Näher1,2, Marvin Kopka2,3, Felix Balzer2
1Digital Global Public Health, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.
Clinical and Translational Science
|July 24, 2024
Summary
Real-world evidence (RWE) trials offer better generalizability than traditional studies. However, the use of random sampling and correction methods in RWE trial design is still limited, hindering full realization of benefits.
Area of Science:
- Clinical Trials
- Real-World Evidence Research
- Biostatistics
Background:
- Real-world evidence (RWE) trials offer enhanced generalizability compared to randomized controlled trials (RCTs), crucial for biological insights and targeted therapies.
- Generalizability in RWE studies is optimized through random sampling or sample correction procedures, especially when using nonrandomly collected real-world data (RWD).
Purpose of the Study:
- To assess the extent to which current RWE trial designs incorporate sampling strategies.
- To investigate the consideration of random sampling or sample correction methods in RWE trial planning and execution.
Main Methods:
- Text mining of publicly available metadata from RWE trial registrations on clinicaltrials.gov, EU-PAS, and OSF-RWE registry.
- Analysis of trends in the reporting of sampling methods and correction procedures in RWE trials from 2002 to 2022.
Main Results:
- The proportion of RWE trial registrations including sampling information rose from 65.27% (2002) to 97.43% (2022).
- Trials employing random sampling increased from 14.79% to 28.30% during the same period.
- The utilization of sample correction procedures for nonrandom samples in RWE trials remains low, increasing from 0.00% to 0.95%.
Conclusions:
- While reporting on sampling in RWE trials has improved, the adoption of robust sampling techniques, including random sampling and correction methods, is not yet widespread.
- The full potential of real-world data to enhance the generalizability of trial findings is not being fully leveraged due to current design limitations.
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