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http://hdl.handle.net/1942/28833
Title: | Strategies for handling missing data in longitudinal studies with questionnaires | Authors: | Nooraee, Nazanin MOLENBERGHS, Geert Ormel, Johan van den Heuvel, Edwin R. |
Issue Date: | 2018 | Publisher: | TAYLOR & FRANCIS LTD | Source: | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 88(17), p. 3415-3436 | Abstract: | Missing data methods, maximum likelihood estimation (MLE) and multiple imputation (MI), for longitudinal questionnaire data were investigated via simulation. Predictive mean matching (PMM) was applied at both item and scale levels, logistic regression at item level and multivariate normal imputation at scale level. We investigated a hybrid approach which is combination of MLE and MI, i.e. scales from the imputed data are eliminated if all underlying items were originally missing. Bias and mean square error (MSE) for parameter estimates were examined. ML seemed to provide occasionally the best results in terms of bias, but hardly ever on MSE. All imputation methods at the scale level and logistic regression at item level hardly ever showed the best performance. The hybrid approach is similar or better than its original MI. The PMM-hybrid approach at item level demonstrated the best MSE for most settings and in some cases also the smallest bias. | Notes: | [Nooraee, Nazanin; van den Heuvel, Edwin R.] Eindhoven Univ Technol, Dept Math & Comp Sci, Eindhoven, Netherlands. [Molenberghs, Geert] Katholieke Univ Leuven, BioStat I, Leuven, Belgium. [Molenberghs, Geert] Univ Hasselt, BioStat I, Diepenbeek, Belgium. [Ormel, Johan] Univ Groningen, Univ Med Ctr Groningen, Interdisciplinary Ctr Psychopathol & Emot Regulat, Groningen, Netherlands. | Keywords: | Fully conditional specification; latent variable models; maximum likelihood; multiple imputation;Fully conditional specification; latent variable models; maximum likelihood; multiple imputation | Document URI: | http://hdl.handle.net/1942/28833 | ISSN: | 0094-9655 | e-ISSN: | 1563-5163 | DOI: | 10.1080/00949655.2018.1520854 | ISI #: | 000445294500009 | Rights: | 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2019 |
Appears in Collections: | Research publications |
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nooraee 1.pdf | Published version | 2.06 MB | Adobe PDF | View/Open |
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