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Mixed-Effects Multivariate Adaptive Splines Models

Heping Zhang, Yale University Department of Epidemiology and Public Health

A mixed-effects multivariate adaptive splines model is proposed to analyze longitudinal or growth curves data that may or may not have been collected through a regular measurement schedule. The MASAL (an acronym for multivariate adaptive splines for the analysis of longitudinal data) algorithm by Zhang (1994, 1997, 1999) is used to determine the nonparametric fixed-effects in the mixed-effects multivariate adaptive splines model. In addition, specific random effects are included in the model. To demonstrate the potential of this new procedure, I will present an analysis of a data set on the effect of cocaine use by pregnant women on the growth of their infants after birth.

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