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

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

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.

Suggested Citation

  • Ariel Alonso & Wim Van der Elst & Geert Molenberghs & Marc Buyse & Tomasz Burzykowski, 2016. "An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference," Biometrics, The International Biometric Society, vol. 72(3), pages 669-677, September.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:3:p:669-677
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    Cited by:

    1. Paul Meyvisch & Chrispin Kambili & Koen Andries & Nacer Lounis & Myriam Theeuwes & Brian Dannemann & An Vandebosch & Wim Van der Elst & Geert Molenberghs & Ariel Alonso, 2018. "Evaluation of six months sputum culture conversion as a surrogate endpoint in a multidrug resistant-tuberculosis trial," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-15, July.

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