Clinical trials are becoming more complex: a machine learning analysis of data from over 16,000 trials
Nigel Markey1, Ben Howitt1, Ilyass El-Mansouri2
1Boston Consulting Group, 80 Charlotte Street, London, W1T 4DF, UK.
Scientific Reports
|February 12, 2024
Summary
Clinical trials are becoming more complex, with longer timelines and low success rates. A new Trial Complexity Score, derived from machine learning analysis of over 16,000 trials, quantifies this trend.
Area of Science:
- Clinical trial management
- Regulatory science
- Health informatics
Background:
- Clinical trials face increasing complexity due to innovations, regulatory changes, and external pressures.
- Despite advancements, clinical trial timelines have lengthened while success rates remain low.
- This has prompted questions about the necessity and impact of clinical trial complexity.
Purpose of the Study:
- To analyze the complexity of clinical trials using a large-scale dataset.
- To develop a quantitative metric for assessing clinical trial complexity.
- To investigate trends in trial complexity across different phases and therapeutic areas.
Main Methods:
- Analysis of protocols and data from over 16,000 clinical trials.
- Application of a machine learning algorithm to assess trial features (e.g., endpoints, inclusion-exclusion criteria).
- Development of a Trial Complexity Score using regression analysis, correlating complexity with trial duration.
Main Results:
- The Trial Complexity Score indicates a substantial increase in clinical trial complexity over time across various phases and therapeutic areas.
- The developed score shows a correlation with overall clinical trial duration.
- Identified key features contributing to trial complexity.
Conclusions:
- Clinical trials are demonstrably becoming more complex.
- Distinguishing between necessary ('good') and unnecessary ('bad') complexity is crucial.
- Exploration of mechanisms to reduce unnecessary complexity in clinical trial design and execution is recommended.
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