SIMBA–a Bayesian decision framework for the identification of optimal biomarker subgroups for cancer basket clinical trials

Motivated by a multi-indication basket trial aiming to assess the efficacy of a novel biomarker-targeted therapy in gastric or gastroesophageal junction (G/GEJ), pancreatic, and other related cancers, we consider a statistical design and decision-making framework for such trials. Typically, the investigational therapy in the trial targets a biomarker that is present in multiple cancer indications, and patients with higher biomarker expression tend to exhibit higher response rates, assuming the targeting biomarkers are over-expressed in tumor cells. To enable information sharing across indications, the proposed SIMBA method introduces a Bayesian hierarchical model that defines positive and negative biomarker subgroups and identifies optimal go/no-go decisions. The operating characteristics of SIMBA are assessed via simulations and compared against existing methods in the literature. Overall, SIMBA is constructed to improve the identification of patient sub-populations who may benefit from biomarker-targeted therapeutics.