Surrogate endpoints are very important in regulatory decision-making in
healthcare, in particular if they can be measured early compared to the
long-term final clinical outcome and act as good predictors of clinical
benefit. Bivariate meta-analysis methods can be used to evaluate surrogate
endpoints and to predict the treatment effect on the final outcome from the
treatment effect measured on a surrogate endpoint. However, candidate surrogate
endpoints are often imperfect, and the level of association between the
treatment effects on the surrogate and final outcomes may vary between
treatments. This imposes a limitation on the pairwise methods which do not
differentiate between the treatments. We develop bivariate network
meta-analysis (bvNMA) methods which combine data on treatment effects on the
surrogate and final outcomes, from trials investigating heterogeneous treatment
contrasts. The bvNMA methods estimate the effects on both outcomes for all
treatment contrasts individually in a single analysis. At the same time, they
allow us to model the surrogacy patterns across multiple trials (different
populations) within a treatment contrast and across treatment contrasts, thus
enabling predictions of the treatment effect on the final outcome for a new
study in a new population or investigating a new treatment. Modelling
assumptions about the between-studies heterogeneity and the network
consistency, and their impact on predictions, are investigated using simulated
data and an illustrative example in advanced colorectal cancer. When the
strength of the surrogate relationships varies across treatment contrasts,
bvNMA has the advantage of identifying treatments for which surrogacy holds,
thus leading to better predictions