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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14704

Title: Establishing normative data for repeated cognitive assessment: a comparison of different statistical methods
Authors: Van der Elst, Wim
Molenberghs, Geert
Van Boxtel, Martin
Jolles, Jelle
Issue Date: 2013
Citation: Behavior research methods, 45 (4), p. 1073-1086
Abstract: Serial cognitive assessment is conducted to monitor changes in the cognitive abilities of patients over time. At present, mainly the regression-based change and the ANCOVA approaches are used to establish normative data for serial cognitive assessment. These methods are straightforward, but they have some severe drawbacks. For example, they can only consider the data of two measurement occasions. In this article, we propose three alternative normative methods that are not hampered by these problems -that is, multivariate regression, the standard linear mixed model (LMM), and the linear mixed model combined with multiple imputation (LMM with MI) approaches. The multivariate regression method is primarily useful when a small number of repeated measurements are taken at fixed time moments. When the data are more unbalanced, the standard LMM and the LMM with MI methods are more appropriate because they allow for a more adequate modeling of the covariance structure. The standard LMM has the advantage that it is easier to conduct and that it does not require a Monte Carlo component. The LMM with MI, on the other hand, has the advantage that it can flexibly deal with missing responses and missing covariate values at the same time. The different normative methods are illustrated on the basis of the data of a large longitudinal study in which a cognitive test (the Stroop Color Word Test) was administered at four measurement occasions (i.e., at baseline and 3, 6, and 12 years later). The results are discussed and suggestions for future research are provided.
Notes: Reprint Address: Van der Elst, W (reprint author). Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat CenStat, Martelarenlaan 42, B-3500 Hasselt, Belgium. E-mail Addresses:Wim.Vanderelst@gmail.com
URI: http://hdl.handle.net/1942/14704
DOI: 10.3758/s13428-012-0305-y
ISI #: 000328272100014
ISSN: 1554-351X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2015
Appears in Collections: Research publications

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