Statistical methods in medical research
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Stat Methods Med Res · Feb 2004
ReviewMixed effects multivariate adaptive splines model for the analysis of longitudinal and growth curve data.
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. ⋯ The analysis of a real example is also presented to illustrate the application and interpretation of MASAL. Open questions are posed for further investigation.
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Stat Methods Med Res · Feb 2004
ReviewFunctional data analysis in longitudinal settings using smoothing splines.
Data in many experiments arise as curves and therefore it is natural to use a curve as a basic unit in the analysis, which is termed functional data analysis (FDA). In longitudinal studies, recent developments in FDA have extended classical linear models and linear mixed effects models to functional linear models (also termed varying-coefficient models) and functional mixed effects models. ⋯ Due to the connection between smoothing splines and linear mixed effects models, functional mixed effects models can be fitted using existing software such as SAS Proc Mixed. A case study is presented as an illustration.