We describe a meta-analysis approach for the evaluation of a potential surrogate marker. Surrogate markers are useful in helping to identify therapeutic mechanisms of action and disease pathogenesis, and for selecting therapies to take forward from phase II to phase III clinical trials. They have also become increasingly important for regulatory purposes by providing a basis for preliminary approval of drugs pending clinical outcome studies. Methodology for evaluating surrogate markers has focused on determining the difference in the effects of two treatments on clinical outcome in an individual clinical trial, and then estimating the proportion of this difference explained by the treatment's effects on the potential marker. Studies are, however, frequently underpowered or cease before they accumulate sufficient evidence to draw strong conclusions about the value of a potential surrogate marker using this approach, and there are also some technical difficulties with the approach. Consideration of the association between the difference in treatment effects on the clinical outcome and the difference in treatment effects on the potential marker over a range of trials provides an alternative means to evaluate a potential marker. We describe a meta-analysis approach using Bayesian methods to model this association. Importantly, this approach enables one to obtain prediction intervals for the true difference in clinical outcome for a given estimated treatment difference in the effect on the potential marker. We illustrate the methodology by applying it to results from studies of the AIDS Clinical Trials Group to assess the value of CD4 T-lymphocyte cell count as a potential surrogate marker for the treatment effects on the development of AIDS or death.