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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/10769

Title: Missing data methods in longitudinal studies: a review
Authors: Ibrahim, Joseph G.
Molenberghs, Geert
Issue Date: 2009
Publisher: SPRINGER
Citation: TEST, 18(1). p. 1-43
Abstract: Incomplete data are quite common in biomedical and other types of research, especially in longitudinal studies. During the last three decades, a vast amount of work has been done in the area. This has led, on the one hand, to a rich taxonomy of missing-data concepts, issues, and methods and, on the other hand, to a variety of data-analytic tools. Elements of taxonomy include: missing data patterns, mechanisms, and modeling frameworks; inferential paradigms; and sensitivity analysis frameworks. These are described in detail. A variety of concrete modeling devices is presented. To make matters concrete, two case studies are considered. The first one concerns quality of life among breast cancer patients, while the second one examines data from the Muscatine children's obesity study.
Notes: [Ibrahim, Joseph G.] Univ N Carolina, Dept Biostat, Chapel Hill, NC 27515 USA. [Molenberghs, Geert] Hasselt Univ, Ctr Stat, Int Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Molenberghs, Geert] Catholic Univ Leuven, B-3590 Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/10769
DOI: 10.1007/s11749-009-0138-x
ISI #: 000264549700001
ISSN: 1133-0686
Category: A1
Type: Journal Contribution
Validation: ecoom, 2010
Appears in Collections: Research publications

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