Evaluation of Surrogate Endpoints in Clinical Trials

rgl, lattice, latticeExtra, survival, nlme, lme4

Wim Van der Elst, Paul Meyvisch, Ariel Alonso & Geert Molenberghs

Wim Van der Elst, Paul Meyvisch, Ariel Alonso, Hannah M. Ensor, Christopher J. Weir & Geert Molenberghs
rgl, lattice, latticeExtra, survival, nlme, lme4,
OrdinalLogisticBiplot, logistf, rms

In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's 7-th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
rgl, lattice, latticeExtra, survival, nlme, lme4,
OrdinalLogisticBiplot, logistf, rms, mixtools

In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.