Some Tests for the Extended Growth Curve Model and Applications in the Analysis of Clustered Longitudinal Data

Jemila S. Hamid () and Sayantee Jana ()
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Jemila S. Hamid: University of Ottawa
Sayantee Jana: Indian Institute of Management Nagpur

Chapter Chapter 7 in Recent Developments in Multivariate and Random Matrix Analysis, 2020, pp 103-122 from Springer

Abstract: Abstract The Growth Curve Model (GCM) is a Generalized Multivariate Analysis of Variance (GMANOVA) model especially useful in the analysis of longitudinal data, growth curves as well as other response curves. The model is a natural extension of the classical Multivariate Analysis of Variance (MANOVA) model and among other assumptions, relies on the assumption that the mean for each group can be represented as a polynomial of degree q. The assumption that the mean over time for all groups follows a polynomial of the same degree is not always satisfied, since individuals across the different groups may respond differently. An excellent scenario is when we have clustered longitudinal data, where the response over time can be represented by polynomials of different degrees. In such situations, the natural extension is to use the Extended Growth Curve Model (EGCM), where one can assume different shapes to represent different groups or clusters. In this paper, we formulate hypotheses motivated by real life scenarios involving clustered longitudinal data, and propose tests that are motivated by residuals in the EGCM. We then mathematically derive the tests and evaluate performances using simulations. We provide real data examples as illustrations.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-56773-6_7

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DOI: 10.1007/978-3-030-56773-6_7

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