Predictive Model Surrogate Control Arm Clinical Trials
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Clinical trials often require large control arms to assess treatment efficacy, which can be inefficient and may deter participants due to the likelihood of receiving a placebo or standard of care, as most subjects receive experimental treatments.
Innovation Solution
A trained and validated predictive model, such as a deep neural network or convolutional neural network, is used to predict medical disorder progression from baseline images, allowing for the reduction or elimination of a control arm by serving as a surrogate, enabling more subjects to receive experimental treatments and reducing the number of participants needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a traditional control arm is used in clinical trials, then treatment efficacy can be assessed, but the number of participants required increases and efficiency decreases
Solution Approach 1:
The patent creates a virtual control arm by copying the characteristics and expected outcomes from historical control data, allowing researchers to simulate control group results without enrolling additional participants. This digital twin approach replaces physical control subjects with computational models that replicate control group behavior patterns.
Solution Approach 2:
An artificial intelligence system serves as an intermediary between the treatment arm and the assessment process, using machine learning algorithms to predict control arm outcomes and compare them against actual treatment results. This AI mediator eliminates the need for direct comparison with physical control subjects.
2Reliability
If a large control arm is used, then statistical power is maintained, but participant recruitment becomes more difficult due to placebo assignment
Solution Approach 1:
The system performs preliminary analysis by pre-processing historical control data into predictive models before the trial begins. This advance preparation allows the virtual control arm to be ready for immediate comparison, maintaining statistical rigor without requiring participants to be assigned to control groups.
Solution Approach 2:
The AI system continuously learns from historical data and refines its predictions during the trial, providing real-time feedback on treatment efficacy. This adaptive feedback mechanism maintains statistical power by dynamically adjusting comparison benchmarks based on accumulated evidence.
Data Source
AI summary
Methods of performing experimental treatments on a cohort of subjects are provided. A predictive model can be utilized to predict progression of a medical disorder or relevant imaging biomarker. The predicted medical disorder progression can be utilized as a control to determine whether an experimental treatment has an effect on the progression of the medical disorder. In some instances, the enrollment of subjects within a control group for clinical experiment is eliminated or reduced.