Bayesian Hierarchical Modelling for Histology-Independent Time-to-Event Outcomes in the NICE Single Technology Appraisal of Pembrolizumab for Solid Tumours with MSI-H/dMMR: External Assessment Group Perspective

Excerpt

Oncological treatments, traditionally based on pathologic classification and organ of origin, are increasingly shifting towards histology-independent targeted therapies, classified based on specific genomic or molecular alterations. This is based on the idea that tumour types with a shared genomic/molecular alteration potentially respond in a similar way to such treatments that aim to interact or bind with the target molecule [1]. The first histology-independent marketing authorisation was granted by the European Medicines Agency in 2019 [2]. The latest European Medicines Agency guidance revision addresses the emergence of indications defined by a common biomarker and histology-independent basket trial designs (i.e. a trial investigating a targeted therapy for multiple histological subtypes with a shared biomarker or mutation) [3]. The European Medicines Agency identified two possible roles of basket trials, including one in early-phase trials. If considered as evidence for licensing decisions, the need to demonstrate sufficient homogeneity is specified: “… sponsors must justify and make it convincingly plausible by clinical and/or pre-clinical data that the interaction with tumour site or histology is negligible and this should also be supported by the final data” [3]. For reimbursement decisions, the National Institute for Health Research HTA Programme commissioned a report in 2020 assessing modelling approaches for histology-independent cancer drugs to inform the National Institute of Health and Care Excellence (NICE) technology appraisals. The report highlighted the greater levels of heterogeneity within the licensed population, use of surrogate endpoints and the usual lack of comparators as possible challenges for using histology-independent basket trials to inform evidence within the appraisal process. For heterogeneity, Bayesian hierarchical modelling (BHM) is considered particularly suited to the assumption that inter-tumour site efficacy is similar within basket trials, representing a middle ground between assuming complete homogeneity (i.e. pooling all tumour sites) and complete heterogeneity (i.e. independent modelling of tumour sites). Bayesian hierarchical modelling therefore accounts for heterogeneity whilst simultaneously leveraging information available from different tumour sites [2]. …