Statistiskt seminarium

Abstrakt finns enbart på engelska: Current psychiatric diagnoses are based primarily on self-reported experiences. Unfortunately, treatments for the diagnoses are not effective for all patients. One hypothesized reason is that "artificial grouping of heterogeneous syndromes with different pathophysiological mechanisms into one disorder." To address this problem, the US National Institute of Mental Health instituted the Research Domain Criteria framework in 2009. This research framework calls for integrating data from many levels of information: genes, cells, molecules, circuits, physiology, behavior, and self-report. Clustering comes to the forefront as a key tool in this effort. In this talk, I present a case study of the use of mixture models to cluster older adults based on measures of sleep from three domains: diary, actigraphy, and polysomnography. Challenges in this effort include the use of mixtures of skewed distributions, a large number of potential clustering variables, and seeking clinically meaningful solutions. We present novel variable selection algorithms, study them by simulation, and demonstrate our methods on the sleep data. This work is joint with Meredith Wallace.