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Quantifying the Effect of the Surrogate Marker by Information Gain
Author(s) -
Qu Yongming,
Case Michael
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.1541-0420.2007.00852_1.x
Subject(s) - surrogate endpoint , information gain , statistics , surrogate model , computer science , predictive marker , surrogate data , artificial intelligence , mathematics , econometrics , medicine , physics , cancer , nonlinear system , quantum mechanics
Summary Statistical validation of a surrogate marker has been studied for more than a decade. Recently, Alonso et al. (2004, Biometrics 60 , 724–728) proposed a quantity called the likelihood reduction factor (LRF) to evaluate the validity of a surrogate marker. However, as pointed out in the present article, the LRF may not correctly validate a surrogate marker. Therefore, a new quantity, the proportion of information gain (PIG) using the Kullback–Leibler information, is proposed. Simulations show that under some model assumptions, the PIG precisely reflects the role of a surrogate marker.