Integration of Virtual Twins and Bayesian ML Algorithms in Efficacy Prediction - A Case Study

Music City Center 

Successful clinical development depends on a thorough understanding of relevant internal and external clinical trial data, along with published literature. By leveraging this information, researchers can gain deeper insights into heterogeneous patient populations and the covariates influencing treatment efficacy, ultimately enhancing the likelihood of program success and facilitating efficient decision-making. Popular methods such as virtual twins along with machine learning (ML), Bayesian methods, causal inference can be utilized to assess treatment effects. The integration of ML techniques with Bayesian methods allows for accurate predictions of treatment efficacy while also providing uncertainty estimates for model outputs, which is essential for informed decision-making and risk assessment. Additionally, some limitations of existing algorithms, such as model misspecification, can be mitigated by incorporating approaches from the increasingly prominent field of causal inference.
In this talk, we will explore the application of virtual twins incorporating Bayesian machine learning techniques to both simulated and real clinical trial data, focusing on their performance from a causal inference perspective. Specifically, we will investigate three machine learning algorithms—Random Forest, elastic net, and artificial neural networks— together with virtual twins to identify heterogeneous subpopulations. Our findings will demonstrate that common challenges faced by traditional ML algorithms, such as low prediction accuracy, overfitting, and insufficient uncertainty estimates, can be effectively addressed through the integration of Bayesian methodologies to improve traditional virtual twin framework. Additionally, we will provide a statistically rigorous uncertainty quantification through conformal prediction interval.

virtual twins

Conformal prediction

Bayesian

Causal inference

Machine learning