Properties of a nonparametric test for early comparison of treatments in clinical trials in the presence of surrogate endpoints.

Overview

A nonparametric test is derived for comparing treatments with respect to the final endpoint in clinical trials in which the final endpoint has been observed for a random subset of patients, but results are available for a surrogate endpoint for a larger sample of patients. The test is an adaptation of the Wilcoxon-Mann-Whitney two-sample test, with an adjustment that involves a comparison of the ranks of the surrogate endpoints between patients with and without final endpoints. The validity of the test depends on the assumption that the patients with final endpoints represent a random sample of the patients registered in the study. This assumption is viable in trials in which the final endpoint is evaluated at a "landmark" timepoint in the patients' natural history. A small sample simulation study demonstrates that the test has a size that is close to the nominal value for all configurations evaluated. When compared with the conventional test based only on the final endpoints, the new test delivers substantial increases in power only when the surrogate endpoint is highly correlated with the true endpoint. Our research indicates that, in the absence of modeling assumptions, auxiliary information derived from surrogate endpoints can provide significant additional information only under special circumstances.