Population-adjusted unanchored indirect comparisons of cancer therapies with borrowing of pan-tumor information.

In clinical research of cancer therapy for rare mutations, trial designs must be adapted to accommodate the typically small sample sizes, and single-arm and basket trials have gained prominence. In this paper, we apply principles of Bayesian hierarchical methods and multilevel network meta-regression to propose a model for a pairwise population-adjusted unanchored indirect comparison of cancer therapies in different tumor types with borrowing of pan-tumor information. An individual-level regression model is defined for the single-arm trial of the intervention for which we have individual patient data. The aggregate data of the other trial for the competing intervention are fitted by integrating the covariate effects at the individual level over its covariate distribution to form the aggregate likelihood. To improve the estimation of the tumor type-specific relative treatment effects, we assume exchangeability reflecting the belief of a pan-tumor effect. The method is illustrated with a case study of adagrasib versus sotorasib in previously treated KRAS^G12C-mutated advanced/metastatic tumors: non-small cell lung cancer (NSCLC), colorectal cancer (CRC), and pancreatic ductal adenocarcinoma (PDAC). Adagrasib was associated with a greater tumor response than sotorasib according to the analyses: The odds ratios were 1.87 (1.21-2.84) for NSCLC; 2.08 (1.22-3.93) for CRC; and 2.02 (1.14-4.05) for PDAC. The analysis illustrated that a reasonably conservative assumption about the degree of similarity can result in more meaningful and interpretable findings. The proposed model allows for population adjustment and information sharing across tumor types when performing an unanchored indirect comparison of interventions for which it is believed a pan-tumor effect holds.