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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24133

Title: Assessing a surrogate predictive value: a causal inference approach
Authors: Alonso, Ariel
Van der Elst, Wim
Meyvisch, Paul
Issue Date: 2017
Publisher: WILEY
Citation: STATISTICS IN MEDICINE, 36(7), p. 1083-1098
Abstract: Several methods have been developed for the evaluation of surrogate endpoints within the causal-inference and meta-analytic paradigms. In both paradigms, much effort has been made to assess the capacity of the surrogate to predict the causal treatment effect on the true endpoint. In the present work, the so-called surrogate predictive function (SPF) is introduced for that purpose, using potential outcomes. The relationship between the SPF and the individual causal association, a new metric of surrogacy recently proposed in the literature, is studied in detail. It is shown that the SPF, in conjunction with the individual causal association, can offer an appealing quantification of the surrogate predictive value. However, neither the distribution of the potential outcomes nor the SPF are identifiable from the data. These 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 used to study the behavior of the SPF on the previous region. The method is illustrated using data from a clinical trial involving schizophrenic patients and a newly developed and user friendly R package Surrogate is provided to carry out the validation exercise. Copyright (c) 2016 John Wiley & Sons, Ltd.
Notes: [Alonso, Ariel] Katholieke Univ Leuven, I BioStat, B-3000 Leuven, Belgium. [Van der Elst, Wim] Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. [Meyvisch, Paul] Johnson & Johnson, Janssen Pharmaceut, Beerse, Belgium.
URI: http://hdl.handle.net/1942/24133
DOI: 10.1002/sim.7197
ISI #: 000395389100003
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2018
Appears in Collections: Research publications

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