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Methods to assess trustworthiness of data from peer-reviewed, published randomised controlled trials
Lyle C Gurrin1, Nicole Au2, Jeremy Nielsen2
1School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia. lgurrin@unimelb.edu.au.
Reproductive Biology and Endocrinology : RB&E
|July 17, 2026
Summary
New quantitative tools enhance research integrity by assessing the trustworthiness of published randomized controlled trials (RCTs). These methods detect data duplication and unrealistic findings, improving scientific evidence reliability.
Area of Science:
- Medical research integrity
- Health evidence reliability
Background:
- Untrustworthy randomized controlled trials (RCTs) pose a significant challenge to medical research.
- Ensuring the reliability of scientific evidence is crucial for informing clinical practice.
Purpose of the Study:
- To explore theory, methods, and tools for assessing research integrity.
- To evaluate the trustworthiness of aggregate and individual-level data from published RCTs.
Main Methods:
- Review of published checklists for trustworthiness assessment.
- Statistical analysis of aggregate and individual participant data.
- Application of artificial intelligence (AI) tools for data analysis.
Main Results:
- Checklists evaluate data authenticity based on timeframe, authors, governance, and plausibility.
- Statistical techniques identify unusual data patterns and realistic data distributions.
- RCT baseline data offers unique opportunities for trustworthiness checks.
Conclusions:
- Novel quantitative tools assess research integrity and data trustworthiness in published RCTs.
- Techniques detect data duplication, questionable practices, and unrealistic study aspects.
- AI-embedded platforms can scale detection of corrupted research and paper mill output.
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