Skedastic Function Model for Prognostic Trial Design
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Solution Overview
Problem
Randomized Controlled Trials (RCTs) face challenges in accurately estimating treatment effects due to uncertainty and the need for large sample sizes, especially when population and design differences exist between current and historical data, leading to inefficiencies and potential biases in trial design and analysis.
Innovation Solution
The development of systems and methods that utilize prognostic models, specifically defining a skedastic function model independently of target trial data, to estimate treatment effects by designing trial parameters, computing standard errors, and updating parameters based on uncertainty, incorporating historical data and digital twins to adjust for prognostic scores and variances, thereby reducing uncertainty and sample size requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RCT design is used, then sample size is large, but statistical power is insufficient and uncertainty is high
Solution Approach 1:
The system performs preliminary actions by defining a skedastic function model using historical data before the target trial. This pre-modeling step captures relationships between covariates and outcomes, allowing the trial to leverage this prior knowledge for more efficient estimation with smaller sample sizes while maintaining statistical power.
Solution Approach 2:
The system changes parameters by adjusting trial design parameters (sample size, allocation ratios, covariate collection) based on the skedastic function model. The model provides heteroskedasticity-consistent standard errors that account for varying uncertainty across different covariate patterns, enabling optimized parameter selection that reduces required sample size while maintaining reliability.
2Productivity
If historical data is used to inform trial design, then efficiency improves, but bias from population differences may be introduced
Solution Approach 1:
The system implements feedback by using the skedastic function model to compute heteroskedasticity-consistent standard errors that reflect the actual uncertainty in treatment effect estimation. This feedback mechanism allows the trial design to account for the quality and applicability of historical data, adjusting weights and sample size allocations to maintain accuracy while leveraging historical efficiency gains.
Solution Approach 2:
The skedastic function model serves as an intermediary between historical data and target trial analysis. It translates historical relationships into adjusted standard errors and weighting schemes that account for population differences, allowing efficient use of historical data without directly introducing bias into the treatment effect estimation.
3Reliability
If prognostic models are applied, then uncertainty quantification improves, but model complexity increases
Solution Approach 1:
The system extracts the essential uncertainty information from complex prognostic models by focusing specifically on the skedastic function that models the variance of outcomes as a function of covariates. This extraction approach captures the critical uncertainty quantification needed for trial design without requiring the full complexity of the underlying prognostic model, simplifying implementation while maintaining reliability.
Data Source
AI summary
Systems and method for estimating treatment effects for a target trial in accordance with embodiments of the invention are illustrated. One embodiment includes a method. The method defines a skedastic function model, wherein defining the skedastic function model is performed independently of data that will be applied to a target trial. The method designs trial parameters for the target trial based in part on the skedastic function model. The method applies the trial parameters to a loss function to derive at least one minimizing coefficient, wherein a minimizing coefficient corresponds to a regression coefficient for an expected outcome to the target trial based on the trial parameters. The method computes standard errors for the at least one minimizing coefficient. The method quantifies, using the standard errors, values for uncertainty associated with the target trial. The method updates the trial parameters according to the uncertainty.