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Mixed effects multivariate adaptive splines model for the analysis of longitudinal and growth curve data

Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, USA, heping.zhang{at}yale.edu

In this article, I review the use of nonparametric methods in the analysis of longitudinal and growth curve data, particularly the multivariate adaptive splines models for the analysis of longitudinal data (MASAL). These methods combine nonparametric techniques (B-splines, kernel smoothing, piecewise polynomials) and models with random effects, and provide fruitful alternatives to mixed effects linear models. Similarities, differences, strengths and limitations among these methods are presented. The analysis of a real example is also presented to illustrate the application and interpretation of MASAL. Open questions are posed for further investigation.

Statistical Methods in Medical Research, Vol. 13, No. 1, 63-82 (2004)
DOI: 10.1191/0962280204sm353ra


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