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Please use this identifier to cite or link to this item: 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
Citation: 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.
URI: http://hdl.handle.net/1942/20872
DOI: 10.1080/02664763.2016.1168370
ISI #: 000394567300005
ISSN: 0266-4763
Category: A1
Type: Journal Contribution
Validation: vabb, 2018
Appears in Collections: Research publications

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