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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/4012

Title: Incomplete hierarchical data
Authors: Beunckens, Caroline
Molenberghs, Geert
Thijs, Herbert
Verbeke, Geert
Issue Date: 2007
Publisher: SAGE PUBLICATIONS LTD
Citation: STATISTICAL METHODS IN MEDICAL RESEARCH, 16(5). p. 457-492
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.
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
URI: http://hdl.handle.net/1942/4012
DOI: 10.1177/0962280206075310
ISI #: 000250441300006
ISSN: 0962-2802
Category: A1
Type: Journal Contribution
Validation: ecoom, 2008
Appears in Collections: Research publications

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