Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?
45 Pages Posted: 13 Jun 2019
Date Written: May 31, 2019
The success of a new drug is assessed within a clinical trial using a primary endpoint, which is typically the true outcome of interest, e.g., overall survival. However, regulators sometimes allow drugs to be approved using a surrogate outcome — an intermediate indicator that is faster or easier to measure than the true outcome of interest, e.g., progression-free survival — as the primary endpoint when there is demonstrable medical need. While using a surrogate outcome (instead of the true outcome) as the primary endpoint can substantially speed up clinical trials and lower costs, it can also result in poor drug approval decisions since the surrogate is not a perfect predictor of the true outcome. In this paper, we propose combining data from both surrogate and true outcomes to improve decision-making within a clinical trial. In contrast to broadly used clinical trial designs that rely on a single primary endpoint, we propose a Bayesian adaptive clinical trial design that simultaneously leverages both observed outcomes to inform trial decisions. We perform comparative statics on the relative benefit of our approach, illustrating the types of diseases and surrogates for which our proposed design is particularly advantageous. Finally, we illustrate our proposed design on metastatic breast cancer. We use a large-scale clinical trial database to construct a Bayesian prior, and simulate our design on a subset of clinical trials. We estimate that our proposed design would yield a 5% increase in trial benefits relative to existing clinical trial designs.
Keywords: Surrogates, Bayesian Adaptive Clinical Trials, Metastatic Breast Cancer
JEL Classification: I18
Suggested Citation: Suggested Citation