Sparse Functional Data with a Periodic Component: Methods and Applications to Psychiatric Data

• Activity Number: 386
• Type: Invited
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: General Methodology
• Author(s): Catherine Ann Sugar*+
• Companies: University of California at Los Angeles
• Keywords: Functional Data Analysis ; Periodic Data ; Bipolar Disorder ; Psychiatry

Abstract

Many psychiatric illnesses have a periodic component, with patients experiencing illness "episodes" of varying length and severity, interspersed with periods of relatively good health. Even when subjects are followed longitudinally over an extended time frame the resulting data are often difficult to analyze, both because of the irregularity in the amplitude, period length and sequencing of events, and because observations tend to be sparse relative to the natural illness cycle. In this talk, we present a functional data analytic approach to such problems. Sparsity is dealt with by borrowing strength across subjects using random effects models, by jointly examining trajectories of multiple outcomes per subject, and by using pre-treatment or other supplementary data to better assess within subject periodicity. The methods will be illustrated with an application to bipolar disorder where the goal is to look at differential treatment effects on length, frequency and severity of manic and depressive episodes.