An information‐theoretic approach for the evaluation of surrogate endpoints based on causal inference. Issue 3 (10th February 2016)

• Record Type: Journal Article
• Authors: Alonso, Ariel; Van der Elst, Wim; Molenberghs, Geert; Buyse, Marc; Burzykowski, Tomasz
• Is Part Of: Biometrics. Volume 72:Issue 3(2016)
• Page Start: 669
• Page End: 677
• Publication Date: 2016-02-10
• Subjects: Causal inference -- Information theory -- Monte Carlo -- Surrogate endpoints; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12483
• Languages: English
• ISSNs: 0006-341X

Abstract

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.