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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20492

Title: Statistical evaluation of surrogate endpoints with examples from cancer clinical trials
Authors: Buyse, Marc
Molenberghs, Geert
Paoletti, Xavier
Oba, Koji
Alonso Abad, Ariel
Van der Elst, Wim
Burzykowski, Tomasz
Issue Date: 2016
Publisher: WILEY-BLACKWELL
Citation: BIOMETRICAL JOURNAL, 58 (1), p. 104-132
Abstract: A surrogate endpoint is intended to replace a clinical endpoint for the evaluation of new treatments when it can be measured more cheaply, more conveniently, more frequently, or earlier than that clinical endpoint. A surrogate endpoint is expected to predict clinical benefit, harm, or lack of these. Besides the biological plausibility of a surrogate, a quantitative assessment of the strength of evidence for surrogacy requires the demonstration of the prognostic value of the surrogate for the clinical outcome, and evidence that treatment effects on the surrogate reliably predict treatment effects on the clinical outcome. We focus on these two conditions, and outline the statistical approaches that have been proposed to assess the extent to which these conditions are fulfilled. When data are available from a single trial, one can assess the "individual level association" between the surrogate and the true endpoint. When data are available from several trials, one can additionally assess the "trial level association" between the treatment effect on the surrogate and the treatment effect on the true endpoint. In the latter case, the "surrogate threshold effect" can be estimated as the minimum effect on the surrogate endpoint that predicts a statistically significant effect on the clinical endpoint. All these concepts are discussed in the context of randomized clinical trials in oncology, and illustrated with two meta-analyses in gastric cancer.
Notes: [Buyse, Marc] IDDI, Cambridge, MA 02138 USA. [Buyse, Marc; Molenberghs, Geert; Van der Elst, Wim; Burzykowski, Tomasz] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat I BioSt, B-3500 Hasselt, Belgium. [Molenberghs, Geert; Alonso, Ariel] Univ Leuven, KU Leuven, Interuniv Inst Biostat & Stat Bioinformat I BioSt, B-3000 Leuven, Belgium. [Paoletti, Xavier] Inst Curie, INSERM U900, Dept Biostat, F-75005 Paris, France. [Oba, Koji] Univ Tokyo, Grad Sch Med, Sch Publ Hlth, Dept Biostat,Bunkyo Ku, Tokyo 1130033, Japan. [Oba, Koji] Univ Tokyo, Interfac Initiat Informat Studies, Bunkyo Ku, Tokyo 1130033, Japan. [Burzykowski, Tomasz] IDDI, B-1340 Louvain La Neuve, Belgium.
URI: http://hdl.handle.net/1942/20492
DOI: 10.1002/bimj.201400049
ISI #: 000367731300008
ISSN: 0323-3847
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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