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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20869

Title: An Information-Theoretic Approach for the Evaluation of Surrogate Endpoints Based on Causal Inference
Authors: Alonso Abad, Ariel
Van der Elst, Wim
Molenberghs, Geert
Buyse, Marc E.
Burzykowski, Tomasz
Issue Date: 2016
Citation: Biometrics, 72(3), p. 669-677
Abstract: 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.
URI: http://hdl.handle.net/1942/20869
DOI: 10.1111/biom.12483
ISI #: 000383369000001
ISSN: 0006-341X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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