English publication

Theunissen, N.C.M., Meulman, J.J., den-Ouden, A.L., Koopman, H.M., Verrips, G.H., Verloove-Vanhorick, S.P., & Wit, J.M. (2003). Changes can be studied when the measurement instrument is different at different time points. Health Services and Outcomes Research Methodology, 4 (2): 109-126 (published may 2004)

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This paper presents a strategy for the analysis of longitudinal data that suffer from differences in measurement instruments over time. For illustration, we used a set of longitudinal Health Status (HS) data from a cohort of preterm born children. The strategy solved three problems: First, commonly known HS domains were not explicitly used. Therefore, expert ratings were used to fit the variables into HS domains. Second, it was unclear which HS variables at 5 years of age matched which variables at 10 years of age. Principal component analyses (PCA) was used to match the data between measurements. Third, the change between points of measurement were estimated by longitudinal PCAs using the matched HS data. The impact of background variables such as gender and birth weight on HS changes was studied. This strategy using an imperfect data set, provided valuable information about the development of HS in preterm born children.


principal component analysis; CATPCA; longitudinal studies; health status; preterm birth;

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Last updated on 12 February, 2015.