STATISTICAL APPROACHES FOR MULTIDIMENSIONAL SLEEP HEALTH: APPLICATION TO ALL-CAUSE MORTALITY IN OLDER ADULTS. (11th November 2018) - Wallace, M L
Abstract: Sleep is multidimensional, characterized by domains such as satisfaction, alertness, timing, efficiency, and duration. However, few studies use a multidimensional sleep health framework in relation to health outcomes. One possible reason is because this approach presents more methodological challenges. Dr. Wallace will demonstrate a range of statistical approaches to studying multidimensional sleep health, including: (1) composite scores quantifying the number of "good" or "bad" sleep domains; (2) multivariable regression; and (3) random forests that can be used to establish the importance of multidimensional sleep health relative to other established risk factors. These approaches will also be compared to a more traditional "unidimensional" approach focusing on individual dimensions of sleep. The benefits and disadvantages of each approach will be demonstrated using a large (N=8588) cohort of older adults aggregated from three publicly available datasets, each of which collected self-report sleep data and up to 15 years follow-up for mortality.