Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/20869
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | ALONSO ABAD, Ariel | - |
dc.contributor.author | VAN DER ELST, Wim | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.contributor.author | BUYSE, Marc | - |
dc.contributor.author | BURZYKOWSKI, Tomasz | - |
dc.date.accessioned | 2016-03-31T15:09:48Z | - |
dc.date.available | 2016-03-31T15:09:48Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Biometrics, 72(3), p. 669-677 | - |
dc.identifier.issn | 0006-341X | - |
dc.identifier.uri | http://hdl.handle.net/1942/20869 | - |
dc.description.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. | - |
dc.description.sponsorship | Financial 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.iso | en | - |
dc.rights | © 2016, The International Biometric Society | - |
dc.subject.other | causal inference; information theory; Monte Carlo; surrogate endpoints | - |
dc.title | An Information-Theoretic Approach for the Evaluation of Surrogate Endpoints Based on Causal Inference | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 677 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 669 | - |
dc.identifier.volume | 72 | - |
local.format.pages | 9 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1111/biom.12483 | - |
dc.identifier.isi | 000383369000001 | - |
local.uhasselt.international | yes | - |
item.contributor | ALONSO ABAD, Ariel | - |
item.contributor | VAN DER ELST, Wim | - |
item.contributor | MOLENBERGHS, Geert | - |
item.contributor | BUYSE, Marc | - |
item.contributor | BURZYKOWSKI, Tomasz | - |
item.accessRights | Restricted Access | - |
item.fullcitation | ALONSO 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.validation | ecoom 2017 | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 0006-341X | - |
crisitem.journal.eissn | 1541-0420 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Alonso_et_al-2016-Biometrics.sup-1.pdf Restricted Access | Supplementary material | 295.34 kB | Adobe PDF | View/Open Request a copy |
Alonso_et_al-2016-Biometrics.pdf Restricted Access | Published version | 169.28 kB | Adobe PDF | View/Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.