Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/416
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kenward, Michael G. | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.contributor.author | THIJS, Herbert | - |
dc.date.accessioned | 2004-10-29T09:01:08Z | - |
dc.date.available | 2004-10-29T09:01:08Z | - |
dc.date.issued | 2003 | - |
dc.identifier.citation | Biometrika, 90(1). p. 53-71 | - |
dc.identifier.issn | 0006-3444 | - |
dc.identifier.uri | http://hdl.handle.net/1942/416 | - |
dc.description.abstract | Recently, pattern-mixture modelling has become a popular tool for modelling incomplete longitudinal data.Such models are under-identified in the sense that, for any drop-out pattern, the data provide no direct information on the distribution of the unobserved outcomes, given the observed ones. One simple way of overcoming this problem, ordinary extrapolation of sufficiently simple pattern-specific models, often produces rather unlikely descriptions; several authors consider identifying restrictions instead. Molenberghs et al. (1998) have constructed identifying restrictions corresponding to missing at random. In this paper, the family of restrictions where drop-out does not depend on future, unobserved observations is identified. The ideas are illustrated using a clinical study of Alzheimer patients | - |
dc.description.sponsorship | We would like to thank David Clayton for helpful discussions. We gratefully acknowledge support from Fonds Wetenschappelijk Onderzoek-Vlaanderen and from Vlaams Instituut voor de Bevordering van het Wetenschappelijk-Technologisch Onderzoek in Industrie. We acknowledge support from Interuniversity Attraction Poles Programme P5/24 of the Belgian State-Federal Office for Scientific, Technical and Cultural Affairs. | - |
dc.format.extent | 1325324 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | BIOMETRIKA TRUST | - |
dc.subject | Longitudinal data | - |
dc.subject | Missing data | - |
dc.subject.other | drop-out; longitudinal data; missing at random; missing data; repeated measurements; selection model | - |
dc.title | Pattern-mixture models with proper time dependence | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 71 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 53 | - |
dc.identifier.volume | 90 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1093/biomet/90.1.53 | - |
dc.identifier.isi | 000181996800005 | - |
item.accessRights | Open Access | - |
item.fullcitation | Kenward, Michael G.; MOLENBERGHS, Geert & THIJS, Herbert (2003) Pattern-mixture models with proper time dependence. In: Biometrika, 90(1). p. 53-71. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2004 | - |
item.contributor | Kenward, Michael G. | - |
item.contributor | MOLENBERGHS, Geert | - |
item.contributor | THIJS, Herbert | - |
crisitem.journal.issn | 0006-3444 | - |
crisitem.journal.eissn | 1464-3510 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
molg02.pdf | Peer-reviewed author version | 1.29 MB | Adobe PDF | View/Open |
a.pdf Restricted Access | Published version | 2.32 MB | Adobe PDF | View/Open Request a copy |
SCOPUSTM
Citations
79
checked on Sep 2, 2020
WEB OF SCIENCETM
Citations
78
checked on May 16, 2024
Page view(s)
70
checked on Sep 6, 2022
Download(s)
220
checked on Sep 6, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.