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Jordan S. Lundeen

BlueChoice HealthPlan of South Carolina



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William Vaughn McCall

Augusta University



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Stephen W. Looney

Augusta University



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176 – Modeling

Methodological Issues in the Functional Data Analysis of Actigraphy Data

Sponsor: Section on Statistical Computing
Keywords: basis functions, missing data, imputation, biomarker, validation, rest-activity data

Jordan S. Lundeen

BlueChoice HealthPlan of South Carolina

William Vaughn McCall

Augusta University

Stephen W. Looney

Augusta University

This presentation 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 basis 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.

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