Speakers & Presentations

Super-Efficient Estimation of Average Treatment Effect based on Randomized Controlled Trial Augmented with External Controls or Observational Study Learning

Mark Van der Laan, University of California at Berkeley

Mark, who pioneered the widely acclaimed targeted maximum likelihood estimator (TMLE) approach for causal inference, will elucidate the validity of the framework in the context of augmenting control arms to generate valid and reliable evidence.

Advancing Clinical Development through Real-World Data, AI and Machine Learning

Issa Dahabreh, Harvard University

Sensitivity Analysis Strategies for Unmeasured Confounding when Integrating External Controls in Randomized Controlled Trials

Mingyang Shan, Eli Lilly

From the perspective of pharmaceutical industry, Mingyang will share pertinent experiences with reference to alternative sensitivity analysis strategies in dealing with the issue of unmeasured confounding when incorporating external controls in RCTs.

Regulatory and Statistical Considerations for Non-randomized Comparative Trials in Drug Development

Pallavi Mishra-Kalyani, US Food and Drug Administration (FDA)

Pallavi will highlight current regulatory thinking in the area and provide salient aspects of relevant guidelines aimed at fostering innovation in drug development.

New Proposals in Clinical Study Design based on Applications of Differential Hermite and Propensity Scores Indices

Javier Cabrera, Rutgers University

Discussion

Demissie Alemayehu, Pfizer

Demissie will provide a synthesis of the ideas presented by the speakers and engage the participants and the audience to generate further insights.