MSR73 Bayesian Hierarchical Models for Indirect Treatment Comparisons of Histology-Independent Therapies for Survival Outcomes

Abstract

Basket trials are increasingly being used to investigate novel therapies targeting rare cancer mutations common to multiple histologies. Analyses of basket trials for histology-independent therapies (HIT) often employ complete pooling of information across histologies to improve power (assuming that outcomes are homogenous across histologies), or no pooling whatsoever. However, a third increasingly used option is application of Bayesian hierarchical models (BHM) to allow for partial pooling of information across histologies, with the amount of pooling dependent on the degree of between-histology heterogeneity. We extend these BHM approaches to indirect treatment comparisons (ITC) for survival endpoints.