Place: 4274 Chamberlin (Refreshments will be served)
Speaker: Amy Cochran, UW Departments of Biostatistics & Medical Informatics
Abstract: Bipolar disorder is a chronic disease of severe mood fluctuation. Longitudinal patterns are central to any patient description, but are condensed into simple attributes and categories. Although these provide a common language for clinicians, they are not supported by empirical evidence. In this talk, I will discuss modeling frameworks for providing patient-level descriptions of longitudinal patterns. Since these frameworks often represent competing hypotheses, e.g. mood is periodic or mood has distinct 'states', I will focus on a key question: how to differentiate between models when only time courses of mood are available? Through statistical analysis, we settle on the idea that BP could arise from an inability for mood to quickly return to normal when perturbed and present a model to embody this idea that can be personalized to individual with bipolar disorder. I will conclude by discussing next steps for translating this work into clinical care.