title: Multivariate cubic spline smoothing in multiple prediction.
abstract: Given longitudinal data for several variables, including a given outcome variable, it is desired to predict the outcome for a specific individual, or more generally experimental unit, in such a way that the predicted value is both accurate and resistant (i.e. has good cross-validation). There are certain data-analytic difficulties associated with long-term multivariate longitudinal data that must be overcome in the prediction process. This paper provides a program written in the Statistical Analysis System (SAS) programming language, based generally on the Roche-Wainer-Thissen stature prediction model, that enables the researcher to overcome these difficulties.
authors.affiliation: Institutionen för Informationsvetenskap, Uppsala Universitet, Uppsala, Sweden. harry.khamis@wright.edu
authors.forename: Harry
authors.initials: H
authors.lastname: Khamis
authors.forename: Michael
authors.initials: M
authors.lastname: Kepler
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D011381:Programming Languages; D012984:Software