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http://hdl.handle.net/1942/4012
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DC Field | Value | Language |
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dc.contributor.author | BEUNCKENS, Caroline | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.contributor.author | THIJS, Herbert | - |
dc.contributor.author | VERBEKE, Geert | - |
dc.date.accessioned | 2007-12-07T14:16:15Z | - |
dc.date.available | 2007-12-07T14:16:15Z | - |
dc.date.issued | 2007 | - |
dc.identifier.citation | STATISTICAL METHODS IN MEDICAL RESEARCH, 16(5). p. 457-492 | - |
dc.identifier.issn | 0962-2802 | - |
dc.identifier.uri | http://hdl.handle.net/1942/4012 | - |
dc.description.abstract | The researcher collecting hierarchical data is frequently confronted with incompleteness. Since the processes governing missingness are often outside the investigator's control, no matter how well the experiment has been designed, careful attention is needed when analyzing such data. We sketch a standard framework and taxonomy largely based on Rubin's work. After briefly touching upon (overly) simple methods, we turn to a number of viable candidates for a standard analysis, including direct likelihood, multiple imputation and versions of generalized estimating equations. Many of these require so-called ignorability. With the latter condition not necessarily satisfied, we also review flexible models for the outcome and missingness processes at the same time. Finally, we illustrate how such methods can be very sensitive to modeling assumptions and then conclude with a number of routes for sensitivity analysis. Attention will be given to the feasibility of the proposed modes of analysis within a regulatory environment. | - |
dc.description.sponsorship | We gratefully acknowledge support from Belgian IUAP/PAI network ‘Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data.’ | - |
dc.language.iso | en | - |
dc.publisher | SAGE PUBLICATIONS LTD | - |
dc.rights | © 2007 SAGE Publications | - |
dc.title | Incomplete hierarchical data | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 492 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 457 | - |
dc.identifier.volume | 16 | - |
local.format.pages | 36 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. Katholieke Univ Leuven, Ctr Biostat, Louvain, Belgium.BEUNCKENS, C, Hasselt Univ, Ctr Stat, Agoralaan 1, B-3590 Diepenbeek, Belgium.caroline.beunckens@uhasselt.be | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1177/0962280206075310 | - |
dc.identifier.isi | 000250441300006 | - |
item.accessRights | Restricted Access | - |
item.fullcitation | BEUNCKENS, Caroline; MOLENBERGHS, Geert; THIJS, Herbert & VERBEKE, Geert (2007) Incomplete hierarchical data. In: STATISTICAL METHODS IN MEDICAL RESEARCH, 16(5). p. 457-492. | - |
item.contributor | BEUNCKENS, Caroline | - |
item.contributor | MOLENBERGHS, Geert | - |
item.contributor | THIJS, Herbert | - |
item.contributor | VERBEKE, Geert | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2008 | - |
crisitem.journal.issn | 0962-2802 | - |
crisitem.journal.eissn | 1477-0334 | - |
Appears in Collections: | Research publications |
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a.pdf Restricted Access | Published version | 547.95 kB | Adobe PDF | View/Open Request a copy |
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