Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning
arXiv:2602.22285v1 Announce Type: new Abstract: Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) according to their likelihood of exhibiting a high rate of dosing errors, using information available prior to trial initiation. Materials and Methods: We constructed a dataset from ClinicalTrials.gov comprising 42,112 CTs. Structured, semi-structured trial data, and unstructured protocol-related free-text data were extracted. CTs were assigned binary labels indicating elevated dosing error rate, derived from adverse event reports, MedDRA terminology, and Wilson confidence intervals. We evaluated an XGBoost model trained on structured features, a ClinicalModernBERT model using textual data, and a simple late-fusion model combining both modalities. Post-hoc probability calibration was applied to enable interpretable, trial-level risk stratification. Results: The late-fusion model achieved the
arXiv:2602.22285v1 Announce Type: new Abstract: Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) according to their likelihood of exhibiting a high rate of dosing errors, using information available prior to trial initiation. Materials and Methods: We constructed a dataset from ClinicalTrials.gov comprising 42,112 CTs. Structured, semi-structured trial data, and unstructured protocol-related free-text data were extracted. CTs were assigned binary labels indicating elevated dosing error rate, derived from adverse event reports, MedDRA terminology, and Wilson confidence intervals. We evaluated an XGBoost model trained on structured features, a ClinicalModernBERT model using textual data, and a simple late-fusion model combining both modalities. Post-hoc probability calibration was applied to enable interpretable, trial-level risk stratification. Results: The late-fusion model achieved the highest AUC-ROC (0.862). Beyond discrimination, calibrated outputs enabled robust stratification of CTs into predefined risk categories. The proportion of trials labeled as having an excessively high dosing error rate increased monotonically across higher predicted risk groups and aligned with the corresponding predicted probability ranges. Discussion: These findings indicate that dosing error risk can be anticipated at the trial level using pre-initiation information. Probability calibration was essential for translating model outputs into reliable and interpretable risk categories, while simple multimodal integration yielded performance gains without requiring complex architectures. Conclusion: This study introduces a reproducible and scalable ML framework for early, trial-level risk stratification of CTs at risk of high dosing error rates, supporting proactive, risk-based quality management in clinical research.
Executive Summary
This study develops a machine learning-based framework for early risk stratification of clinical trials according to their likelihood of exhibiting high dosing errors. Using a dataset of 42,112 clinical trials, the researchers evaluated various models, including an XGBoost model, a ClinicalModernBERT model, and a late-fusion model combining both modalities. The late-fusion model achieved the highest AUC-ROC of 0.862 and enabled robust stratification of trials into predefined risk categories. The study demonstrates the feasibility of anticipating dosing error risk at the trial level using pre-initiation information and highlights the importance of probability calibration for translating model outputs into reliable risk categories. The findings have significant implications for proactive, risk-based quality management in clinical research.
Key Points
- ▸ The study develops a machine learning-based framework for early risk stratification of clinical trials.
- ▸ The late-fusion model achieved the highest AUC-ROC of 0.862 and enabled robust stratification of trials.
- ▸ Probability calibration is essential for translating model outputs into reliable risk categories.
Merits
Strength in Model Performance
The late-fusion model achieved a high AUC-ROC of 0.862, indicating strong performance in distinguishing between high and low dosing error rates.
Scalability and Reproducibility
The study introduces a reproducible and scalable machine learning framework, which can be easily adapted to other clinical trials and datasets.
Demerits
Limited Generalizability
The study's findings may not be generalizable to other types of clinical trials or datasets, which could limit the framework's applicability.
Dependence on Pre-Initiation Data
The framework's performance relies on the availability and quality of pre-initiation data, which may not always be feasible or accurate.
Expert Commentary
This study makes a significant contribution to the field of clinical trial quality management by introducing a machine learning-based framework for early risk stratification of dosing errors. The late-fusion model's performance is impressive, and the findings have important implications for proactive quality management. However, the study's limitations, such as limited generalizability and dependence on pre-initiation data, need to be addressed in future research. The expert community will need to carefully evaluate the framework's performance in different contexts and ensure its scalability and reproducibility.
Recommendations
- ✓ Future studies should aim to improve the framework's generalizability by incorporating diverse datasets and clinical trials.
- ✓ Researchers should investigate the use of alternative machine learning algorithms and architectures to enhance the framework's performance.
