In immuno-oncology, developing combination therapies to overcome resistance to single agent or induce synergistic effects has become a new focus. To accelerate the screening process to identify promising combinations based on objective response rates, we propose a Bayesian adaptive Umbrella Trial design to simultaneously evaluate combinations of an investigational compound with different backbones, where information borrowing across combinations is allowed to increase trial efficiency. A robust borrowing approach is developed to strike a balance between borrowing and not borrowing by accounting for different configurations of homogeneity of treatment effects using Bayesian model averaging. Unlike existing methods that use the response rates to measure the degree of homogeneity by assuming all arms share a common control rate, an advantage of our approach is that it uses relative treatment effects to determine the degree of homogeneity by adjusting for different control effects across combinations. In the proposed design, Bayesian adaptive interim analyses are implemented to drop futile combinations and graduate early efficacious combinations. Simulation studies demonstrate that the proposed design with robust information borrowing outperforms some existing approaches. It improves power when treatment effects are homogeneous and maintains reasonable arm-wise Type I error rates when heterogeneity is present across combinations. for this article are available online.

Predictive and prognostic biomarkers play an important role in personalized medicine to determine strategies for drug evaluation and treatment selection. In the context of continuous biomarkers, identification of an optimal cutoff for patient selection can be challenging due to limited information on biomarker predictive value, the biomarker’s distribution in the intended use population, and the complexity of the biomarker relationship to clinical outcomes. As a result, prespecified candidate cutoffs may be rationalized based on biological and practical considerations. In this context, adaptive enrichment designs have been proposed with interim decision rules to select a biomarker-defined subpopulation to optimize study performance. With a group sequential design as a reference, the performance of several proposed adaptive designs are evaluated and compared under various scenarios (e.g., sample size, study power, enrichment effects) where type I error rates are well controlled through closed testing procedures and where subpopulation selections are based upon the predictive probability of trial success. It is found that when the treatment is more effective in a subpopulation, these adaptive designs can improve study power substantially. Furthermore, we identified one adaptive design to have generally higher study power than the other designs under various scenarios.

Panitumumab is a fully human monoclonal antibody that targets the epidermal growth factor receptor. Results from the primary analysis of a phase 3, randomized, controlled study showed a statistically significant improvement in progression-free survival for patients receiving panitumumab; however, overall survival was confounded by best supportive care (BSC) patients that crossed over to panitumumab therapy after disease progression. Three post hoc analyses are presented that approximate the panitumumab overall survival treatment effect in both the all-randomized and wild-type (WT) KRAS populations by using the BSC patients with mutant (MT) KRAS as the comparator group to discount the effect of crossover from BSC to panitumumab. The primary post hoc analysis showed a median overall survival of 6.4 months for all KRAS-evaluable patients randomized to panitumumab versus 4.4 months for patients with MT KRAS tumors randomized to BSC, yielding an adjusted hazard ratio (95 % CI) of 0.764 (0.598-0.977). Similar results were observed for the two secondary post hoc analyses. These analyses suggest a positive treatment effect of panitumumab in both the overall and WT KRAS patient populations consistent with an improvement in overall survival relative to BSC.