Statistical Methods for Randomized Clinical Trials with Information Borrowing from External Controls

Music City Center 

Room: CC-109 

External controls (ECs) from historical randomized clinical trials (RCTs) or real-world data (RWD) can be used to construct or augment the comparator arm to support regulatory and healthcare decision-making. This has seen increasing use in pediatrics, rare diseases, and diseases with high unmet need. However, heterogeneity between the RCT and RWD stemming from covariate shift, posterior drift, or unmeasured confounding can lead to biased treatment effect estimates and incorrect decision-making. To enable robust decision-making, careful considerations need to be given to the design and analysis of EC trials to mitigate potential biases. This course will provide an overview of the statistical framework and challenges in EC trials, robust Bayesian and Frequentist methods to adaptively borrow ECs using data similarity metrics to mitigate biases due to data heterogeneity, developments in sensitivity analysis to evaluate the robustness of 'no unmeasured confounding' and other untestable assumptions in EC settings, and considerations in the power analysis and sample size determination for EC designs. Simulated case studies and R code will be integrated throughout the course to accompany the methodology and concepts presented. A basic understanding of clinical trials, GLM, survival analysis, hypothesis testing, and Bayesian statistics will be helpful for this course.

Biopharmaceutical Section