Recent Advances in Computing, Optimization, and Causal Inference for Adaptive Clinical Trials

The implementation of Bayesian adaptive trials in clinical research presents unique practical challenges, particularly when integrating these methodologies into software products. Commercial packages can be somewhat constraining and inflexible, and often fail to incorporate modern tools for design optimization and causal inference. By contrast, the writing of bespoke computer code for each new design is often inefficient and unacceptably work-intensive. In this session, we will hear from 4 speakers who have extensive experience in both the methodological and computational aspects of this tradeoff. Speaker 1 (Carlin) will provide an overview of recent advances in computational power to manage the complexity of the Bayesian adaptive approach, contrasting it with frequentist approaches. He will then describe a new platform whose application programming interface (API) facilitates effective communication among diverse stakeholders, and presents complex Bayesian results in an intuitive manner. Group sequential methods implemented with moderate-dimensional alpha- and beta-spending functions emerge as key to this process. Speaker 2 (Wathen) will offer an in-depth exploration of the unique challenges of adaptive platform designs, and discuss practical solutions for navigating this intricate landscape using a R package called OCTOPUS. This talk will emphasize the critical importance of simulating the exact platform trial one plans to conduct, and of accounting for the addition and removal of new treatments over time, as well as other sources of variation that may impact the performance of the platform. Speaker 3 (Pryluk) will introduce a commercial platform incorporating a novel ensemble estimation approach that leverages causal machine learning methods to enhance the detection and assessment of heterogeneity in adaptive trials. The framework uses conformal prediction to assess uncertainty in its ML estimates for finite samples, facilitating a more nuanced understanding of how different patient subgroups respond to treatments. Speaker 4 (Roychoudhury) will present experience at his firm to date with a new approach that largely eschews commercial software packages and instead embraces the free open-source software movement, driven by R and Rmarkdown. Finally, the discussant (Thompson) will summarize the presentations, offer a regulatory perspective on the commercial and open-source approaches, give her view on the future of AI in regulatory science, and suggest areas for future work. The proposed session is timely (as the cost of clinical trials continues to grow and the free software movement continues to burgeon), and should appeal to a large collection of biostatisticians and trialists hoping to use modern adaptive and causal inference tools on a routine basis, especially in rare and pediatric drug development.