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.
|Author||Wim Van der Elst, Paul Meyvisch, Ariel Alonso, Hannah M. Ensor, Christopher J. Weir & Geert Molenberghs|
|Date of publication||2018-06-09 15:46:34 UTC|
|Maintainer||Wim Van der Elst <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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