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Transitional modeling of experimental longitudinal data with missing values

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  • Mark Rooij

    (Leiden University)

Abstract

Longitudinal categorical data are often collected using an experimental design where the interest is in the differential development of the treatment group compared to the control group. Such differential development is often assessed based on average growth curves but can also be based on transitions. For longitudinal multinomial data we describe a transitional methodology for the statistical analysis based on a distance model. Such a distance approach has two advantages compared to a multinomial regression model: (1) sparse data can be handled more efficiently; (2) a graphical representation of the model can be made to enhance interpretation. Within this approach it is possible to jointly model the observations and missing values by adding a new category to the response variable representing the missingness condition. This approach is investigated in a Monte Carlo simulation study. The results show this is a promising way to deal with missing data, although the mechanism is not yet completely understood in all cases. Finally, an empirical example is presented where the advantages of the modeling procedure are highlighted.

Suggested Citation

  • Mark Rooij, 2018. "Transitional modeling of experimental longitudinal data with missing values," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(1), pages 107-130, March.
    • Handle: RePEc:spr:advdac:v:12:y:2018:i:1:d:10.1007_s11634-015-0226-6
    DOI: 10.1007/s11634-015-0226-6
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    References listed on IDEAS

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    6. Hsiu-Ting Yu & Mark Rooij, 2013. "Model Selection for the Trend Vector Model," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 338-369, October.
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