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

Ariel Alonso, Wim Van der Elst, Geert Molenberghs, Marc Buyse and Tomasz Burzykowski

Biometrics, 2016, vol. 72, issue 3, 669-677

Abstract: type="main" xml:lang="en">

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.

Date: 2016
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