title: An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference.
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.
authors: I-BioStat, KU Leuven, B-3000 Leuven, Belgium. ariel.alonsoabad@kuleuven.be.
authors: Ariel
authors: A
authors: Alonso
authors: I-BioStat, Universiteit Hasselt, B-3590 Diepenbeek, Belgium.
authors: Wim
authors: W
authors: Van der Elst
authors: I-BioStat, KU Leuven, B-3000 Leuven, Belgium.|I-BioStat, Universiteit Hasselt, B-3590 Diepenbeek, Belgium.
authors: Geert
authors: G
authors: Molenberghs
authors: I-BioStat, Universiteit Hasselt, B-3590 Diepenbeek, Belgium.|International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.
authors: Marc
authors: M
authors: Buyse
authors: I-BioStat, Universiteit Hasselt, B-3590 Diepenbeek, Belgium.|International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.
authors: Tomasz
authors: T
authors: Burzykowski
mesh_terms: D015415:Biomarkers; D015984:Causality; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination / Q000706:statistics & numerical data*; D005901:Glaucoma / Q000175:diagnosis; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic
chemical_list: D015415:Biomarkers
keywords: Causal inference; Information theory; Monte Carlo; Surrogate endpoints