An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference

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...

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Bibliographic Details
Published in:Biometrics 2016-09, Vol.72 (3), p.669-677
Main Authors: Alonso, Ariel, Van der Elst, Wim, Molenberghs, Geert, Buyse, Marc, Burzykowski, Tomasz
Format: Article
Language:English
Subjects:
Biomarkers
BIOMETRIC METHODOLOGY
Biometrics
Causal inference
Causality
Computer Simulation
Data Interpretation, Statistical
Endpoint Determination - statistics & numerical data
Glaucoma
Glaucoma - diagnosis
Humans
Inference
Information theory
Models, Statistical
Monte Carlo
Monte Carlo Method
Monte Carlo simulation
Randomized Controlled Trials as Topic
Surrogate endpoints
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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.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12483