生物识别与生物统计学杂志

This article examines several methodological issues we have encountered when using functional data analysis (FDA) to analyze actigraphy data. For example, we discuss and compare methods used for handling missing actigraphy data, and how to determine the optimal number of basic functions to use when applying FDA. Curves fit to actigraphy data must take on non-negative values, so we also discuss how to restrict FDA curves so that they have no negative values. The methods and issues we discuss are illustrated using actigraphy data from our study of the utility of a rest-activity biomarker to predict responsiveness to antidepressants.