Generative Models for Synthetic Control Arms in Clinical Trials
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Solution Overview
Problem
Conducting Randomized Controlled Trials (RCTs) is expensive, time-consuming, and sometimes unethical due to the need for large numbers of human subjects, and existing methods for reducing subject numbers have limitations such as population and design mismatches in historical data usage.
Innovation Solution
The use of generative models to create 'digital subjects' or 'digital twins' that mimic human subjects' characteristics, allowing for the simulation of trial data and reducing the number of required human subjects by optimizing trial design and increasing statistical power through prognostic score generation and variance estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RCTs are conducted with large numbers of human subjects, then statistical power and reliability are improved, but cost, time consumption, and ethical concerns increase
Solution Approach 1:
The patent applies preliminary action by generating synthetic control data using generative models before the actual trial begins. This pre-generated synthetic control arm allows for sample size optimization and statistical power estimation in advance, reducing the need for large concurrent control groups and thereby reducing time consumption while maintaining statistical power.
Solution Approach 2:
The patent uses copying by creating synthetic control subjects through generative models that replicate the characteristics and outcomes of historical control subjects. These synthetic copies serve as virtual control arms, replacing or supplementing actual human control subjects, thus reducing the number of human subjects needed while preserving statistical validity.
2Productivity
If historical control data is used to reduce sample size, then time and cost are reduced, but population mismatches and design inconsistencies increase bias
Solution Approach 1:
The patent applies parameter changes by using generative models to transform historical control data into synthetic control data that matches the specific population characteristics and trial design parameters of the current study. By adjusting parameters such as demographic distributions, baseline characteristics, and outcome distributions during the synthetic data generation process, the method eliminates population mismatches while maintaining trial efficiency.
Solution Approach 2:
The patent implements feedback by iteratively refining the synthetic control data generation process based on comparisons between synthetic and actual control characteristics. The generative model is trained and adjusted using feedback from historical data to ensure the synthetic control arm accurately reflects the target population, thereby improving measurement precision while maintaining productivity.
3Object-affected harmful factors
If the number of human subjects is reduced, then ethical concerns and cost are reduced, but statistical power and reliability decrease
Solution Approach 1:
The patent uses copying to create synthetic control subjects that replicate the statistical properties of human control subjects without requiring actual human participation. These synthetic copies provide the necessary statistical power and reliability for treatment effect estimation while eliminating the need for additional human subjects, thereby addressing ethical concerns without sacrificing reliability.
Solution Approach 2:
The patent introduces an intermediary element - the generative model - that mediates between historical control data and the current trial analysis. This intermediary transforms historical data into synthetic control data that can be used in the current trial context, allowing for reduced human subject enrollment while maintaining statistical power through the mediating role of the generative model.
4Quantity of substance
If sample size is minimized, then cost and time are reduced, but precision of treatment effect estimation deteriorates
Solution Approach 1:
The patent applies preliminary action by using generative models to pre-generate synthetic control data and perform sample size optimization before the actual trial. This allows for determining the minimum necessary sample size that maintains treatment effect estimation precision, thereby minimizing the number of subjects required while preserving measurement precision.
Data Source
AI summary
Systems and methods for designing random control trials in accordance with embodiments of the invention are illustrated. One embodiment includes a method for designing a target random control trial. The method includes steps for generating a set of prognostic scores for a set of samples, computing a first correlation between the set of prognostic scores and a set of outcomes for the set of samples, computing a first variance for the set of outcomes for the set of samples, estimating a second correlation and a second variance for a target random control trial, and determining a set of target trial parameters based on the first and second correlations and the first and second variances.