Contributions to Variable Selection in Complexly Sampled Case-control Models, Epidemiology of 72-hour Emergency Department Readmission, and Out-of-site Migration Rate Estimation Using Pseudo-tagged Longitudinal Data
Date of Award
Doctor of Philosophy (PhD)
Computational and Data Sciences
William G. Wright
This work consists of three different projects.
In the first project, I analyze complexly sampled survey data, representative of the US population, to determine what lifestyle behaviors and notions held by participants are most significant with having had a cancer diagnosis. A logistic regression model was built using automatic variable selection with forward selection with backwards elimination. Our results show that sunscreen usage, level of agreeing with the statement "behaviors can affect high blood pressure", age, intent to eat more or less fruit, average daily hours spent watching tv or playing video games, and level of agreeing with the statement "I would rather not know my chances of getting cancer" were significant variables associated with a having had a cancer diagnosis.
In the second project, I developed a novel method for tracking untagged organisms over a 20-year period, data collected at 6-month intervals. Our results showed that the staying rates, emigration/mortality rates, and immigration rates were approximately 50%. We also found that 44.1% of the limpets emigrate/die within their first 6-month time interval.
In the third project, I investigated the most significant predictors of a return to the Emergency Department within 72 hours, with a focus on adult patients with a respiratory condition. High return rates are a burden to both the Emergency Department and patients. We used a dataset extracted from a database containing billions of patient visits and implemented a nested mixed effects model to determine the most significant predictors. There were 20 risk factors found, including demographic variables, diagnostic conditions, and respiratory conditions.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
K. Anderson, "Contributions to variable selection in complexly sampled case- control models, epidemiology of 72-hour Emergency Department readmission, and out-of-site migration rate estimation using pseudo- tagged longitudinal data," Ph.D. dissertation, Chapman University, Orange, CA, 2019. https://doi.org/10.36837/chapman.000120