Defense Date
6-24-2019
Graduation Date
Summer 8-10-2019
Availability
Immediate Access
Submission Type
thesis
Degree Name
MS
Department
Computational Mathematics
School
McAnulty College and Graduate School of Liberal Arts
Committee Chair
Dr. Frank D'Amico
Committee Member
Dr. John Kern
Committee Member
Dr. Stacy Levine
Keywords
Logistic Regression, Ordinal Regression, Clinical Research, Premature Birth, Proportional Odds Model, Vital Statistics Natality Birth Data, NCHS
Abstract
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.
Language
English
Recommended Citation
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
Included in
Applied Statistics Commons, Biostatistics Commons, Multivariate Analysis Commons, Statistical Models Commons, Vital and Health Statistics Commons