title: Surrogate marker evaluation from an information theory perspective.
abstract: The last 20 years have seen lots of work in the area of surrogate marker validation, partly devoted to frame the evaluation in a multitrial framework, leading to definitions in terms of the quality of trial- and individual-level association between a potential surrogate and a true endpoint (Buyse et al., 2000, Biostatistics 1, 49-67). A drawback is that different settings have led to different measures at the individual level. Here, we use information theory to create a unified framework, leading to a definition of surrogacy with an intuitive interpretation, offering interpretational advantages, and applicable in a wide range of situations. Our method provides a better insight into the chances of finding a good surrogate endpoint in a given situation. We further show that some of the previous proposals follow as special cases of our method. We illustrate our methodology using data from a clinical study in psychiatry.
authors: Center for Statistics, Hasselt University, 3590 Diepenbeek, Belgium. ariel.alonso@uhasselt.be
authors: Ariel
authors: A
authors: Alonso
authors: Geert
authors: G
authors: Molenberghs
mesh_terms: D014150:Antipsychotic Agents / Q000627:therapeutic use; D015415:Biomarkers / Q000032:analysis; D002986:Clinical Trials as Topic; D019277:Entropy; D006801:Humans; D007257:Information Theory; D015233:Models, Statistical; D015203:Reproducibility of Results; D018967:Risperidone / Q000627:therapeutic use; D012559:Schizophrenia / Q000188:drug therapy
chemical_list: D014150:Antipsychotic Agents; D015415:Biomarkers; D018967:Risperidone