title: Statistical evaluation of surrogate endpoints with examples from cancer clinical trials.
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.
authors: International Drug Development Institute (IDDI), 185 Alewife Brook Parkway, Suite 410, Cambridge, MA, 02138, USA.|Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium.
authors: Marc
authors: M
authors: Buyse
authors: Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium.|Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), KU Leuven-University of Leuven, Kapucijnenvoer 35, 3000, Leuven, Belgium.
authors: Geert
authors: G
authors: Molenberghs
authors: Department of Biostatistics, INSERM U900, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France.
authors: Xavier
authors: X
authors: Paoletti
authors: Department of Biostatistics, School of Public Health, Graduate School of Medicine, and Interfaculty Initiative in Information Studies, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
authors: Koji
authors: K
authors: Oba
authors: Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), KU Leuven-University of Leuven, Kapucijnenvoer 35, 3000, Leuven, Belgium.
authors: Ariel
authors: A
authors: Alonso
authors: Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium.
authors: Wim
authors: W
authors: Van der Elst
authors: Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium.|International Drug Development Institute (IDDI), avenue provinciale 30, 1340, Louvain-la-Neuve, Belgium.
authors: Tomasz
authors: T
authors: Burzykowski
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic; D018572:Disease-Free Survival; D006801:Humans; D013274:Stomach Neoplasms / Q000188:drug therapy* / Q000378:metabolism*
chemical_list: D014408:Biomarkers, Tumor
keywords: Individual level association; Surrogate endpoint; Surrogate threshold effect; Trial level association