A Software Platform Combining Statistical Rigor and Causal Machine Learning to Enhance Heterogeneity Detection in Clinical Trials and to inform clinical trial design

Music City Center 

Understanding and assessing heterogeneity in clinical trials is essential for personalized medicine and optimizing treatment strategies. This talk introduces a software platform that employs novel ensemble approaches, leveraging causal machine-learning methods to enhance the detection and assessment of heterogeneity in clinical trials while maintaining statistical guarantees. The new ensemble methods integrate multiple estimators to enhance prediction stability and performance - e.g., Stacked X-Learner which uses the X-Learner with model stacking for estimating the nuisance functions, and Consensus Based Averaging (CBA), which averages only the models with highest internal agreement.
Causal machine learning excels at identifying and interpreting relationships between variables, enabling a nuanced understanding of how different patient subgroups respond to treatments. However, selecting the optimal causal ML method is challenging due to frequent disagreements among algorithms, each with unique advantages and limitations. Additionally, many causal ML methods lack well-understood uncertainty estimates, especially in the finite samples common in clinical trials. Our platform addresses these challenges by implementing a framework for estimating uncertainty in finite samples and integrating a weighted combination of different algorithms through Bayesian or Frequentist approaches that account for model uncertainty. This fusion results in robust performance across a wide range of data generation processes.
The user-friendly interface supports commercial use throughout the entire life cycle, from data ingestion and model building to result interpretation and actionable decision-making, including report generation using LLMs.
Moreover, the model output from the heterogeneity analysis can then be used in the other module of the platform to inform simulation analysis of the next clinical trial, whether it's an adaptive or fixed design.

causal inference