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http://hdl.handle.net/1942/24133
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DC Field | Value | Language |
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dc.contributor.author | Alonso, Ariel | - |
dc.contributor.author | VAN DER ELST, Wim | - |
dc.contributor.author | MEYVISCH, Paul | - |
dc.date.accessioned | 2017-08-07T13:12:54Z | - |
dc.date.available | 2017-08-07T13:12:54Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | STATISTICS IN MEDICINE, 36(7), p. 1083-1098 | - |
dc.identifier.issn | 0277-6715 | - |
dc.identifier.uri | http://hdl.handle.net/1942/24133 | - |
dc.description.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. | - |
dc.description.sponsorship | The research leading to these results has received funding from the European Seventh Framework program [FP7 2007 - 2013] under grant agreement no. 602552. | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.rights | Copyright © 2016 John Wiley & Sons, Ltd | - |
dc.subject.other | surrogate endpoint; causal inference; sensitivity analysis; R package Surrogate | - |
dc.subject.other | surrogate endpoint; causal inference; sensitivity analysis; R package Surrogate | - |
dc.title | Assessing a surrogate predictive value: a causal inference approach | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 1098 | - |
dc.identifier.issue | 7 | - |
dc.identifier.spage | 1083 | - |
dc.identifier.volume | 36 | - |
local.format.pages | 16 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.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. | - |
local.publisher.place | HOBOKEN | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1002/sim.7197 | - |
dc.identifier.isi | 000395389100003 | - |
item.fullcitation | Alonso, Ariel; VAN DER ELST, Wim & MEYVISCH, Paul (2017) Assessing a surrogate predictive value: a causal inference approach. In: STATISTICS IN MEDICINE, 36(7), p. 1083-1098. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2018 | - |
item.contributor | Alonso, Ariel | - |
item.contributor | VAN DER ELST, Wim | - |
item.contributor | MEYVISCH, Paul | - |
item.accessRights | Restricted Access | - |
crisitem.journal.issn | 0277-6715 | - |
crisitem.journal.eissn | 1097-0258 | - |
Appears in Collections: | Research publications |
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alonso 1.pdf Restricted Access | Published version | 527.83 kB | Adobe PDF | View/Open Request a copy |
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