McAnulty College and Graduate School of Liberal Arts
Dr. Frank D'Amico
Dr. John Kern
Dr. Stacy Levine
Logistic Regression, Ordinal Regression, Clinical Research, Premature Birth, Proportional Odds Model, Vital Statistics Natality Birth Data, NCHS
Premature birth has been identified as the single greatest cause of death worldwide in children under the age of five. This thesis will implement binary logistic regression and proportional odds ordinal logistic regression to predict different levels of premature birth and identify associated risk factors. The models will be built from the Center for Disease Control and Prevention's 2014 Vital Statistics Natality Birth Data containing nearly 4 million live births within the United States. Odds ratios and confidence intervals on risk factors were produced utilizing binary logistic regression.
Elwood, C. (2019). Identifying Risk Factors Related to Premature Birth Through Binary Logistic and Proportional Odds Ordinal Logistic Regression (Master's thesis, Duquesne University). Retrieved from https://dsc.duq.edu/etd/1803