Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review
By David B. Fogel. Contemporary Clinical Trials Communications, 2018
832 citations recorded by the source corpus.
Clinical trials are time consuming, expensive, and often burdensome on patients. Clinical trials can fail for many reasons. This survey reviews many of these reasons and offers insights on opportunities for improving the likelihood of creating and executing successful clinical trials. Literature from the past 30 years was reviewed for relevant data. Common patterns in reported successful trials are identified, including factors regarding the study site, study coordinator/investigator, and the effects on participating patients. Specific instances where artificial intelligence can help improve clinical trials are identified.
Subfields: Economics and Econometrics, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/34de57ded8601c34b45594b7983a02062ef514f5
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Citations in the corpus
- Artificial Intelligence for Clinical Trial Design. (2019, methodology): “A detailed summary of NLP techniques applicable to clinical trial design is provided in a recent review by Fogel [19].”
- Artificial intelligence in drug discovery and development (2020, neutral): “86% of failure cases [102].”
- Machine learning applications in drug development (2019, background): “Although efforts have been made in order to tackle these issues (as shown by the 2019 figures of success rates between Phases II and III), there is still room for improvement in terms of study planning, as suggested in [11], or designing more insightful preclinical testing [1].”
- ROC Analysis of Biomarker Combinations in Fragile X Syndrome-Specific Clinical Trials: Evaluating Treatment Efficacy via Exploratory Biomarkers (2025, unknown): “…biomarkers to index nuanced physiological patterns may allow for more robust measurement at the individual level that will enhance relationships to clinically relevant outcomes (i.e., changes in observable behaviors) and be robust against consequences of data loss for individual measures [24 – 27].”