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Surrogate Marker Evaluation from an Information Theory Perspective
Author(s) -
Alonso Ariel,
Molenberghs Geert
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00634.x
Subject(s) - surrogate endpoint , biostatistics , computer science , perspective (graphical) , interpretation (philosophy) , surrogate data , point (geometry) , artificial intelligence , medicine , mathematics , pathology , geometry , programming language , public health , physics , nonlinear system , quantum mechanics
Summary 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.