Identifying Risk Factors Related to Premature Birth Through Binary Logistic and Proportional Odds Ordinal Logistic Regression

<p>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.</p>

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2019

Author dc:creator

• Elwood, Clayton

Contributors dc:contributor

• 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
• Applied Statistics
• Biostatistics
• Multivariate Analysis
• Statistical Models
• Vital and Health Statistics

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/1803

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-2833