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Predicting Phase 1 Lymphoma Clinical Trial Durations Using Machine Learning: An In-Depth Analysis and Broad Application Insights

Overview
Journal Clin Pract
Publisher MDPI
Specialty General Medicine
Date 2024 Jan 22
PMID 38248431
Authors
Affiliations
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Abstract

Lymphoma diagnoses in the US are substantial, with an estimated 89,380 new cases in 2023, necessitating innovative treatment approaches. Phase 1 clinical trials play a pivotal role in this context. We developed a binary predictive model to assess trial adherence to expected average durations, analyzing 1089 completed Phase 1 lymphoma trials from clinicaltrials.gov. Using machine learning, the Random Forest model demonstrated high efficacy with an accuracy of 0.7248 and an ROC-AUC of 0.7677 for lymphoma trials. The difference in the accuracy level of the Random Forest is statistically significant compared to the other alternative models, as determined by a 95% confidence interval on the testing set. Importantly, this model maintained an ROC-AUC of 0.7701 when applied to lung cancer trials, showcasing its versatility. A key insight is the correlation between higher predicted probabilities and extended trial durations, offering nuanced insights beyond binary predictions. Our research contributes to enhanced clinical research planning and potential improvements in patient outcomes in oncology.

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References
1.
Schwager E, Jansson K, Rahman A, Schiffer S, Chang Y, Boverman G . Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome. NPJ Digit Med. 2021; 4(1):133. PMC: 8429640. DOI: 10.1038/s41746-021-00505-5. View

2.
Vazquez J, Abdelrahman S, Byrne L, Russell M, Harris P, Facelli J . Using supervised machine learning classifiers to estimate likelihood of participating in clinical trials of a de-identified version of ResearchMatch. J Clin Transl Sci. 2021; 5(1):e42. PMC: 8057403. DOI: 10.1017/cts.2020.535. View

3.
Roberts Jr T, Goulart B, Squitieri L, Stallings S, Halpern E, Chabner B . Trends in the risks and benefits to patients with cancer participating in phase 1 clinical trials. JAMA. 2004; 292(17):2130-40. DOI: 10.1001/jama.292.17.2130. View

4.
Camerlingo N, Vettoretti M, Sparacino G, Facchinetti A, Mader J, Choudhary P . Design of clinical trials to assess diabetes treatment: Minimum duration of continuous glucose monitoring data to estimate time-in-ranges with the desired precision. Diabetes Obes Metab. 2021; 23(11):2446-2454. PMC: 8518626. DOI: 10.1111/dom.14483. View

5.
Sargent D, Conley B, Allegra C, Collette L . Clinical trial designs for predictive marker validation in cancer treatment trials. J Clin Oncol. 2005; 23(9):2020-7. DOI: 10.1200/JCO.2005.01.112. View