Avoiding the Surrogate Paradox: An Empirical Framework for Assessing Assumptions

The use of surrogate markers to replace a primary outcome in clinical trials has the potential to allow earlier decisions about the effectiveness of a treatment when a direct measurement of the primary outcome is difficult to obtain. However, the surrogate paradox, which occurs when a treatment has a positive effect on the surrogate marker but a negative effect on the primary outcome, may lead researchers to make incorrect conclusions about the treatment benefit. In this paper, we propose a formal nonparametric framework to empirically examine and test assumptions that ensure avoidance of the surrogate paradox. For each assumption, we propose a nonparametric hypothesis test, formally derive the properties of the test, and analyze its performance in finite samples in a variety of simulation settings. We apply our proposed testing framework to data from the the Diabetes Prevention Program clinical trial.