Leveraging the power of AI/ML to enhance statistical inference in clinical trials: opportunities and insights

Music City Center 

The increasingly competitive landscape of drug development is motivating accelerated timelines and more efficient processes. Simultaneously, the rapid evolution of Artificial Intelligence (AI) has revolutionized the development landscape, thus presenting emergent opportunities for increasing development efficiency. In particular, new causal inference methodologies such as TMLE, DML, and GRF leverage AI/ML capabilities while preserving statistical inference of target estimands, which is crucial in both clinical reporting and internal decision-making. These methods are anticipated to help accelerate study timeline and conserve resource as well as aid in internal decision-making. We will discuss some of these industry trends as well as Sanofi efforts to fill identified gaps. In particular, we present efforts to industrialize processes for utilization as well as indication-specific simulation and evaluation of candidate methodologies.

Our principal message is that for practical use in clinical trials, causal ML methods can be evaluated and compared only in the context of a specific application and only taking into consideration the anticipated multivariate distribution for the target indication and population.

Causal Machine Learning

Simulation

Pharmaceutical development