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http://hdl.handle.net/1942/20872
Title: | Multiple imputation for ordinal longitudinal data with nonmonotone missing data patterns | Authors: | Kombo, A.Y. Mwambi, H. MOLENBERGHS, Geert |
Issue Date: | 2016 | Source: | Journal of applied statistics, 44 (2), p. 270-287 | Abstract: | Missing data often complicate the analysis of scientific data. Multiple imputation is a general purpose technique for analysis of datasets with missing values. The approach is applicable to a variety of missing data patterns but often complicated by some restrictions like the type of variables to be imputed and the mechanism underlying the missing data. In this paper, the authors compare the performance of two multiple imputation methods, namely fully conditional specification and multivariate normal imputation in the presence of ordinal outcomes with monotone missing data patterns. Through a simulation study and an empirical example, the authors show that the two methods are indeed comparable meaning any of the two may be used when faced with scenarios, at least, as the ones presented here. | Keywords: | longitudinal data; ordinal outcome; monotone missing data patterns; fullyconditional specification; multivariate normal imputation; proportional odds model | Document URI: | http://hdl.handle.net/1942/20872 | ISSN: | 0266-4763 | e-ISSN: | 1360-0532 | DOI: | 10.1080/02664763.2016.1168370 | ISI #: | 000394567300005 | Rights: | © 2016 Informa UK Limited, trading as Taylor & Francis Group | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2018 vabb 2018 |
Appears in Collections: | Research publications |
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481.pdf Restricted Access | Published version | 1.8 MB | Adobe PDF | View/Open Request a copy |
Revision_ordinalJAPS.pdf | Peer-reviewed author version | 361.57 kB | Adobe PDF | View/Open |
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