A SuperLearner Approach to Predict Run-In Selection in Clinical Trials.
Corrado Lanera1, Paola Berchialla2, Giulia Lorenzoni1
1Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan, 18, 35121 Padova, Italy.
Computational and Mathematical Methods in Medicine
|September 21, 2022
Summary
Machine learning (ML) models can simulate clinical trial run-in periods, reducing patient numbers, time, and costs. This approach optimizes patient selection for better trial efficiency.
Area of Science:
- Clinical Trial Design
- Machine Learning in Healthcare
- Biostatistics
Background:
- Defining representative study samples is crucial for clinical trial validity.
- Traditional run-in periods increase patient numbers, trial duration, and costs.
- Leveraging a-priori data can potentially eliminate the need for run-in periods.
Purpose of the Study:
- To explore the use of machine learning (ML) to simulate clinical trial run-in processes.
- To construct individual predictions of therapy response probability using patient characteristics.
- To develop an ML model capable of optimizing patient selection and reducing trial inefficiencies.
Main Methods:
- An ensemble model of 26 machine learning algorithms was developed.
- The model was trained and validated on data from twin randomized clinical trials.
- The SuperLearner (SL) algorithm was employed to mimic a run-in process.
Main Results:
- The ensemble ML model demonstrated strong performance in simulating the run-in period.
- SuperLearner achieved over 70% sensitivity for the Verum (Treatment) arm.
- The Positive Predictive Value (PPP) reached 80%, indicating reliable predictions.
Conclusions:
- The developed ML ensemble model shows significant potential for simulating clinical trial run-in periods.
- This approach can effectively optimize patient selection, minimizing run-in time and associated costs.
- ML-driven patient selection algorithms can enhance the efficiency of conducting clinical trials in similar settings.
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