Surrogate marker evaluation from an information theory perspective.
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.
Alonso
Ariel
A
Center for Statistics, Hasselt University, 3590 Diepenbeek, Belgium. ariel.alonso@uhasselt.be
Molenberghs
Geert
G
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
D014150:Antipsychotic Agents; D015415:Biomarkers; D018967:Risperidone