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 
BURZYKOWSKI, Tomasz 
Issue Date: 2016
Source: 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.
Keywords: causal inference; information theory; Monte Carlo; surrogate endpoints
Document URI: http://hdl.handle.net/1942/20869
ISSN: 0006-341X
e-ISSN: 1541-0420
DOI: 10.1111/biom.12483
ISI #: 000383369000001
Rights: © 2016, The International Biometric Society
Category: A1
Type: Journal Contribution
Validations: ecoom 2017
Appears in Collections:Research publications

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