Biostatistics short course: Incorporating Functional Data into Statistical Models

In this short course, Todd Ogden, PhD, Columbia University, will discuss incorporating functional data into statistical models. Functional data is all around us. Vast quantities of data are routinely gathered from accelerometers and other wearable devices, smartphones, imaging modalities, and many other sources. The term “functional data” refers to any data that can be thought of as multiple observations over some continuum. Examples includes near-infrared spectra, growth curves, 2D or 3D images, time series of ecological momentary assessments (EMA), density functions or histograms, data from electroencephalography (EEG) or magnetic resonance spectroscopy (MRS) studies, and many others. To make the best use of such data, it is necessary to adapt statistical models and techniques to take advantage of the particular structure of functional data. This tutorial will survey some of the advances that have been made and provide examples of analyses that make good use of functional data. It will focus primarily on concepts and interpretation rather than on mathematical or computational details.

Prerequisites:

The main focus of this tutorial will be on necessary concepts underlying functional data analysis and on practical “how to” understanding rather than on mathematical details of the methodologies. Participants should have some basic background in multiple linear regression and knowledge of basic calculus. Some exposure to mathematical statistics is helpful but not required.

Speaker:

Todd Ogden, PhD, is a faculty member in the Department of Biostatistics at Columbia University where he collaborates extensively with researchers in psychiatry, neuroscience, and other fields. These collaborations have provided many opportunities to work on the analysis of real-world functional data, including instances of imaging data and spectral data, and thus much of his recent research has involved statistical methodology for functional data. He earned both a BS degree (in applied math) and a PhD (in statistics) at Texas A&M.