Univariate Versus Multivariate Surrogates in the Single-Trial Setting

In spite of medical and methodological advances, the identification of good surrogate endpoints has remained a challenging endeavor. This may, at least partially, be attributable to the fact that most researchers have only focused on univariate surrogates endpoints. In the present work, we argue in favor of using multivariate surrogates and introduce two new complementary metrics to assess their validity. The first one, the so-called individual causal association, quantifies the association between the individual causal treatment effects on the multivariate surrogate and true endpoints, while the second one quantifies the treatment-corrected association between the multivariate surrogate and the true endpoint outcomes. The newly proposed methodology is implemented in the R package Surrogate and a Web Appendix, detailing how the analysis can be conducted in practice, is provided. Supplementary materials for this article are available online.

Publication year:2019

Keywords:Causal inference, Information theory, Multivariate surrogate endpoints.

BOF-keylabel:yes

IOF-keylabel:yes

BOF-publication weight:1

CSS-citation score:1

Authors from:Higher Education

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