Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20869
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dc.contributor.authorAlonso Abad, Ariel-
dc.contributor.authorVAN DER ELST, Wim-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorBUYSE, Marc-
dc.contributor.authorBURZYKOWSKI, Tomasz-
dc.date.accessioned2016-03-31T15:09:48Z-
dc.date.available2016-03-31T15:09:48Z-
dc.date.issued2016-
dc.identifier.citationBiometrics, 72(3), p. 669-677-
dc.identifier.issn0006-341X-
dc.identifier.urihttp://hdl.handle.net/1942/20869-
dc.description.abstractIn 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.-
dc.description.sponsorshipFinancial support from the IAP research network #P7/06 of the Belgian Government (Belgian Science Policy) is gratefully acknowledged. This research has also received funding from the European Seventh Framework programme [FP7 2007-2013] under grant agreement 602552. The authors gratefully acknowledge Dr David Musch, Coordinating Center Director and Brenda Gillespie, Study Statistician for providing the data from the CIGTS study.-
dc.language.isoen-
dc.rights© 2016, The International Biometric Society-
dc.subject.othercausal inference; information theory; Monte Carlo; surrogate endpoints-
dc.titleAn Information-Theoretic Approach for the Evaluation of Surrogate Endpoints Based on Causal Inference-
dc.typeJournal Contribution-
dc.identifier.epage677-
dc.identifier.issue3-
dc.identifier.spage669-
dc.identifier.volume72-
local.format.pages9-
local.bibliographicCitation.jcatA1-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.1111/biom.12483-
dc.identifier.isi000383369000001-
item.fulltextWith Fulltext-
item.accessRightsRestricted Access-
item.contributorAlonso Abad, Ariel-
item.contributorBURZYKOWSKI, Tomasz-
item.contributorMOLENBERGHS, Geert-
item.contributorBUYSE, Marc-
item.contributorVAN DER ELST, Wim-
item.fullcitationAlonso Abad, Ariel; VAN DER ELST, Wim; MOLENBERGHS, Geert; BUYSE, Marc & BURZYKOWSKI, Tomasz (2016) An Information-Theoretic Approach for the Evaluation of Surrogate Endpoints Based on Causal Inference. In: Biometrics, 72(3), p. 669-677.-
item.validationecoom 2017-
crisitem.journal.issn0006-341X-
crisitem.journal.eissn1541-0420-
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