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An information‐theoretic approach for the evaluation of surrogate endpoints based on causal inference
Author(s) -
Alonso Ariel,
Van der Elst Wim,
Molenberghs Geert,
Buyse Marc,
Burzykowski Tomasz
Publication year - 2016
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/biom.12483
Subject(s) - causal inference , computer science , inference , metric (unit) , surrogate model , identifiability , surrogate data , machine learning , parametric statistics , artificial intelligence , data mining , econometrics , mathematics , statistics , operations management , physics , nonlinear system , quantum mechanics , economics
Summary In this work a new metric of surrogacy, the so‐called individual causal association (ICA), is introduced using information‐theoretic concepts and a causal inference model for a binary surrogate and true endpoint. The ICA has a simple and appealing interpretation in terms of uncertainty reduction and, in some scenarios, it seems to provide a more coherent assessment of the validity of a surrogate than existing measures. The identifiability issues are tackled using a two‐step procedure. In the first step, the region of the parametric space of the distribution of the potential outcomes, compatible with the data at hand, is geometrically characterized. Further, in a second step, a Monte Carlo approach is proposed to study the behavior of the ICA on the previous region. The method is illustrated using data from the Collaborative Initial Glaucoma Treatment Study. A newly developed and user‐friendly R package Surrogate is provided to carry out the evaluation exercise.