  Surrogate endpoints play an important role in drug development when they can
be used to measure treatment effect early compared to the final clinical
outcome and to predict clinical benefit or harm. Such endpoints are assessed
for their predictive value of clinical benefit by investigating the surrogate
relationship between treatment effects on the surrogate and final outcomes
using meta-analytic methods. When surrogate relationships vary across treatment
classes, such validation may fail due to limited data within each treatment
class. In this paper, two alternative Bayesian meta-analytic methods are
introduced which allow for borrowing of information from other treatment
classes when exploring the surrogacy in a particular class. The first approach
extends a standard model for the evaluation of surrogate endpoints to a
hierarchical meta-analysis model assuming full exchangeability of surrogate
relationships across all the treatment classes, thus facilitating borrowing of
information across the classes. The second method is able to relax this
assumption by allowing for partial exchangeability of surrogate relationships
across treatment classes to avoid excessive borrowing of information from
distinctly different classes. We carried out a simulation study to assess the
proposed methods in nine data scenarios and compared them with subgroup
analysis using the standard model within each treatment class. We also applied
the methods to an illustrative example in colorectal cancer which led to
obtaining the parameters describing the surrogate relationships with higher
precision.
