Longitudinal Functional Additive Model with Continuous Proportional Outcomes

• Activity Number: 299
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Survey Research Methods Section
• Author(s): Haocheng Li* and Raymond Carroll and Sarah Kozey-Keadle
• Companies: University of Calgary and Texas A&M University and National Cancer Institute
• Keywords: Continuous proportions ; Functional data ; Longitudinal data ; Mixed-effects model ; Penalized splines ; Principal components

Abstract

Motivated by the data obtained from the BodyMedia FIT device, we take a functional data approach for longitudinal studies with continuous proportional outcomes. The functional structure depends on three factors. In our three-factor model, the regression structures are specified as curves measured at various factor-points with random effects that have a correlation structure. The random curve for the continuous factor is summarized using a few important principal components. The difficulties in handling the continuous proportion variables are solved by using a quasilikelihood type approximation. We develop an efficient algorithm to fit the model, which involves the selection of the number of principal components. The method is evaluated empirically by a simulation study.