TY - JOUR
T1 - Establishing normative data for repeated cognitive assessment: A comparison of different statistical methods
AU - Van der Elst, Wim
AU - Molenberghs, Geert
AU - Van Boxtel, Martin P. J.
AU - Jolles, Jelle
PY - 2013/12
Y1 - 2013/12
N2 - 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 straight-forward, 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 points. 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.
AB - 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 straight-forward, 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 points. 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.
KW - Serial testing
KW - Norms
KW - Practice effects
KW - Longitudinal data
KW - Linear mixed model
KW - Multiple imputation
KW - Stroop Color Word Test
U2 - 10.3758/s13428-012-0305-y
DO - 10.3758/s13428-012-0305-y
M3 - Article
C2 - 23344738
SN - 1554-351X
VL - 45
SP - 1073
EP - 1086
JO - Behavior Research Methods
JF - Behavior Research Methods
IS - 4
ER -