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http://hdl.handle.net/1942/21812
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DC Field | Value | Language |
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dc.contributor.author | Satty, A. | - |
dc.contributor.author | Mwambi, H. | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.date.accessioned | 2016-07-18T13:49:41Z | - |
dc.date.available | 2016-07-18T13:49:41Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | STATISTICAL METHODOLOGY, 24, p. 12-27 | - |
dc.identifier.issn | 1572-3127 | - |
dc.identifier.uri | http://hdl.handle.net/1942/21812 | - |
dc.description.abstract | This paper compares the performance of weighted generalized estimating equations (WGEEs), multiple imputation based on generalized estimating equations (Ml-GEES) and generalized linear mixed models (GLMMs) for analyzing incomplete longitudinal binary data when the underlying study is subject to dropout. The paper aims to explore the performance of the above methods in terms of handling dropouts that are missing at random (MAR). The methods are compared on simulated data. The longitudinal binary data are generated from a logistic regression model, under different sample sizes. The incomplete data are created for three different dropout rates. The methods are evaluated in terms of bias, precision and mean square error in case where data are subject to MAR dropout. In conclusion, across the simulations performed, the MI-GEE method performed better in both small and large sample sizes. Evidently, this should not be seen as formal and definitive proof, but adds to the body of knowledge about the methods' relative performance. In addition, the methods are compared using data from a randomized clinical trial. (C) 2014 Elsevier B.V. All rights reserved. | - |
dc.description.sponsorship | Geert Molenberghs gratefully acknowledges support from IAP research Network P7/06 of the Belgian Government (Belgian Science Policy). Ali Satty and Henry Mwambi gratefully acknowledge post-doctoral funding from the University of KwaZulu-Natal for AS under which the current work was done. | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.rights | © 2014 Elsevier B.V. All rights reserved. | - |
dc.subject.other | Multiple imputation GEE; Weighted GEE; Generalized linear mixed model (GLMM); Incomplete longitudinal binary outcome; Missing at random (MAR) | - |
dc.subject.other | multiple imputation GEE; weighted GEE; generalized linear mixed model (GLMM); incomplete longitudinal binary outcome; missing at random (MAR) | - |
dc.title | Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 27 | - |
dc.identifier.spage | 12 | - |
dc.identifier.volume | 24 | - |
local.format.pages | 16 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | [Satty, A.; Mwambi, H.] Univ KwaZulu Natal, Sch Math Stat & Comp Sci, Private Bag X01, ZA-3209 Pietermaritzburg, South Africa. [Molenberghs, G.] Hasselt Univ, I BioStat, B-3500 Hasselt, Belgium. [Molenberghs, G.] Univ Leuven, KU Leuven, B-3000 Leuven, Belgium. | - |
local.publisher.place | AMSTERDAM | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1016/j.stamet.2014.10.002 | - |
dc.identifier.isi | 000370216600002 | - |
item.fullcitation | Satty, A.; Mwambi, H. & MOLENBERGHS, Geert (2015) Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout. In: STATISTICAL METHODOLOGY, 24, p. 12-27. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2017 | - |
item.contributor | Satty, A. | - |
item.contributor | Mwambi, H. | - |
item.contributor | MOLENBERGHS, Geert | - |
item.accessRights | Restricted Access | - |
crisitem.journal.issn | 1572-3127 | - |
Appears in Collections: | Research publications |
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satty 1.pdf Restricted Access | Published version | 411.36 kB | Adobe PDF | View/Open Request a copy |
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