A marginal approach to reduced-rank penalized spline smoothing with application to multilevel functional data.

ABSTRACT: Multilevel functional data is collected in many biomedical studies. For example, in a study of the effect of Nimodipine on patients with subarachnoid hemorrhage (SAH), patients underwent multiple 4-hour treatment cycles. Within each treatment cycle, subjects' vital signs were reported every 10 minutes. This data has a natural multilevel structure with treatment cycles nested within subjects and measurements nested within cycles. Most literature on nonparametric analysis of such multilevel functional data focus on conditional approaches using functional mixed effects models. However, parameters obtained from the conditional models do not have direct interpretations as population average effects. When population effects are of interest, we may employ marginal regression models. In this work,

SUBMITTER: Chen H 

PROVIDER: S-EPMC3909538 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature