Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9581
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dc.contributor.authorTILAHUN ESHETE, Abel-
dc.contributor.authorASSAM NKOUIBERT, Pryseley-
dc.contributor.authorALONSO ABAD, Ariel-
dc.contributor.authorMOLENBERGHS, Geert-
dc.date.accessioned2009-05-01T10:15:46Z-
dc.date.availableNO_RESTRICTION-
dc.date.issued2008-
dc.identifier.citationJOURNAL OF BIOPHARMACEUTICAL STATISTICS, 18(2). p. 326-341-
dc.identifier.issn1054-3406-
dc.identifier.urihttp://hdl.handle.net/1942/9581-
dc.description.abstractOne of the paradigms for surrogate marker evaluation in clinical trials is based on employing data from several clinical trials: the meta-analytic approach. It was originally developed for continuous outcomes by means of the linear mixed model, but other situations are of interest. One such situation is when both outcomes are binary. Although joint models have been proposed for this setting, they are cumbersome in the sense of computationally complex and of producing validation measures that are, unlike in the Gaussian case, not of an R2 type (Burzykowski et al., 2005). A way to put these problems to rest is by employing information theory, already applied in the continuous case (Alonso and Molenberghs, 2007). In this paper, the information-theoretic approach is applied to the case of binary surrogate and true endpoints. Its use is illustrated using a case study in acute migraine and its performance, relative to existing methods, assessed by means of a simulation study. Because the usefulness of a method critically depends, among others, on the availability of software, a SAS implementation accompanies the methodological work.-
dc.description.sponsorshipThe authors gratefully acknowledge the financial support from the IAP research Network P6/03 of the Belgian Government (Belgian Science Policy).-
dc.language.isoen-
dc.publisherTAYLOR & FRANCIS INC-
dc.rightsCopyright © Taylor & Francis Group, LLC-
dc.subject.otherhierarchical model; meta-analysis; pseudo-likelihood; random-effects model; surrogate endpoint-
dc.subject.otherhierarchical model; meta-analysis; pseudo-likelihood; random-effects model; surrogate endpoint-
dc.titleInformation theory-based surrogate marker evaluation from several randomized clinical trials with binary endpoints, using SAS-
dc.typeJournal Contribution-
dc.identifier.epage341-
dc.identifier.issue2-
dc.identifier.spage326-
dc.identifier.volume18-
local.format.pages16-
local.bibliographicCitation.jcatA1-
dc.description.notes[Tilahun, Abel; Pryseley, Assam; Alonso, Ariel; Molenberghs, Geert] Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium.-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1080/10543400701697190-
dc.identifier.isi000253763000008-
item.fulltextWith Fulltext-
item.validationecoom 2009-
item.contributorASSAM NKOUIBERT, Pryseley-
item.contributorALONSO ABAD, Ariel-
item.contributorTILAHUN ESHETE, Abel-
item.contributorMOLENBERGHS, Geert-
item.fullcitationTILAHUN ESHETE, Abel; ASSAM NKOUIBERT, Pryseley; ALONSO ABAD, Ariel & MOLENBERGHS, Geert (2008) Information theory-based surrogate marker evaluation from several randomized clinical trials with binary endpoints, using SAS. In: JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 18(2). p. 326-341.-
item.accessRightsOpen Access-
crisitem.journal.issn1054-3406-
crisitem.journal.eissn1520-5711-
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