A method to assess the proportion of treatment effect explained by a surrogate endpoint

Randomized clinical trials are the standard for evaluating new drugs, devices and procedures. Traditional clinical trials entail not only considerable expense, but require considerable time to complete. The use of surrogate endpoints constitutes an effort to control cost and completion time for clinical trials. We propose a method to quantify the proportion of treatment effect explained by a surrogate endpoint based on a general model setting which includes the commonly used linear, logistic and Cox regression models. The interpretation of this quantitative measure is facilitated by graphical displays. To reduce the variability associated with the estimate, a meta-analytic approach is proposed based on random effects models. An example using real clinical trial data is given to illustrate the proposed procedures.