Virtual RCT Inclusion Design Using Digital Subjects for Rare Populations
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Solution Overview
Problem
Conducting randomized controlled trials (RCTs) for unique or rare patient populations is expensive, time-consuming, and unethical, especially when recruiting human subjects.
Innovation Solution
Utilizing generative models to create digital subjects that mimic specific patient populations, optimizing inclusion criteria through a cost function that balances population size and treatment effect, and implementing virtual RCTs to enhance statistical power and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human subjects are recruited for RCTs on unique/rare patient populations, then treatment safety and efficacy can be assessed, but the cost, time consumption and ethical concerns increase significantly
Solution Approach 1:
The patent creates digital twins (virtual copies) of human subjects that replicate physiological characteristics and responses. These digital twins are generated from real patient data and can be used in virtual RCTs to assess treatment safety and efficacy without recruiting actual human subjects, thereby reducing time consumption while maintaining assessment reliability
Solution Approach 2:
The system performs preliminary actions by generating and validating digital twin models before conducting actual RCTs. Virtual trials are conducted using these pre-prepared digital subjects to identify promising treatments and refine study designs, reducing the time and resources needed for subsequent human subject recruitment
2Reliability
If human subjects are recruited for RCTs on unique/rare patient populations, then treatment safety and efficacy can be assessed, but cost increases significantly
Solution Approach 1:
Digital twins serve as cost-effective substitutes for expensive human subject recruitment. The virtual subjects are generated from existing data and can be reused across multiple trial scenarios, significantly reducing the financial cost while maintaining the ability to assess treatment safety and efficacy
Solution Approach 2:
The digital twin platform provides multi-functionality by serving multiple purposes: it can be used for virtual RCTs, treatment optimization, patient stratification, and trial design validation. This universal application reduces overall research costs by eliminating the need for separate studies for each objective
3Loss of time
If the number of human subjects is reduced in RCTs, then cost and time are reduced, but statistical power may be compromised
Solution Approach 1:
Digital twins provide sufficient statistical power by creating large populations of virtual subjects that can be generated on demand. These synthetic subjects maintain the statistical properties of real patient populations, enabling robust hypothesis testing and treatment effect estimation without the time constraints of recruiting limited human subjects
Solution Approach 2:
The system changes parameters by allowing flexible adjustment of digital twin characteristics and trial conditions. Researchers can simulate various sample sizes, treatment dosages, and patient subgroups to optimize statistical power while minimizing time consumption, finding the optimal balance between these competing objectives
Data Source
AI summary
One embodiment includes a method for limiting an eligible population for a randomized controlled trial. The method generates panel data for a plurality of digital subjects. The panel data for a given digital subject includes a pre-trial characteristic corresponding to the given digital subject, to be tracked in a virtual RCT. The method derives a preliminary estimate for inclusion criteria used in the virtual RCT, wherein the preliminary estimate includes an upper boundary and a lower boundary on the pre-trial characteristic. The method combines an inclusion function and an interest function to create a cost function. The inclusion function approximates the preliminary estimate for the inclusion criteria as soft constraints. The interest function maps a conditional distribution of potential values to an interest quantity. The method updates the preliminary estimate to derive an updated estimate for the inclusion criteria by optimizing the cost function with respect to the preliminary estimate.