27: Bayesian Analyses and Design of Aggregated Group Sequential N-of-1 Clinical Trials

Music City Center 

N-of-1 trials offer a personalized approach to clinical research, allowing for the evaluation of individualized treatments through repeated crossover designs. Traditional hierarchical models assume a common treatment effect distribution, which may overlook the unique characteristics of distinct patient subgroups. We proposed two methods: Bayesian clustering and a Bayesian mixed approach that combines hierarchy and clustering. These methods dynamically group patients with similar responses while allowing for individual variation. Through extensive simulations, we evaluate the impact of different grouping thresholds on clustering accuracy. The results indicate that our mixed modeling approach outperforms traditional hierarchical methods by reducing bias and enhancing the identification of subgroups. This research advances Bayesian N-of-1 trial models and contributes to the field of precision medicine.

N-of-1 trial 

Biopharmaceutical Section