R package Surrogate. 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.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Flórez, Alvaro Jóse; Alonso Abad, Ariel; Molenberghs, Geert; Van Der Elst, Wim: Generating random correlation matrices with fixed values: an application to the evaluation of multivariate surrogate endpoints (2020)
- Huang, Yu-Min: Binary surrogates with stratified samples when weights are unknown (2019)
- Molenberghs, Geert; Verbeke, Geert; Demétrio, Clarice G. B.: Hierarchical models with normal and conjugate random effects: a review (2017)
- Alonso, Ariel; Van der Elst, Wim; Molenberghs, Geert; Buyse, Marc; Burzykowski, Tomasz: An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference (2016)
- Buyse, Marc; Molenberghs, Geert; Paoletti, Xavier; Oba, Koji; Alonso, Ariel; van der Elst, Wim; Burzykowski, Tomasz: Statistical evaluation of surrogate endpoints with examples from cancer clinical trials (2016)
- Alonso, Ariel; van der Elst, Wim; Molenberghs, Geert; Buyse, Marc; Burzykowski, Tomasz: On the relationship between the causal-inference and meta-analytic paradigms for the validation of surrogate endpoints (2015)