Optimizing Combination Therapies Using a Bayesian Adaptive Design with a Two-dimensional NDLM

Music City Center 

The 2023 American Heart Association Guidelines have identified combination therapies as an important knowledge gap and an area of future research likely to offer the best chance of success for delayed cerebral ischemia (DCI) in patients with an aneurysmal spontaneous subarachnoid hemorrhage (aSAH). In this talk, we present an optimized Bayesian adaptive design to identify the best combination of Cilostazol and Human Albumin using a two-dimensional normal dynamic linear model. This design is shown to be smaller, stronger, faster, and benefit more trial participants than fixed and adaptive designs that use an independent model. Further, the two-dimensional approach avoids the difficulty of prespecifying the order of combination therapies required in a one-dimensional normal dynamic linear model.

response adaptive randomization

smoothing ratio

phase II trial 

Biopharmaceutical Section