Advisory Committee Chair
David B Allison
Advisory Committee Members
Charles R Katholi
Charles D Cowan
Nengjun Yi
Olivia Thomas
Edward W Gregg
Document Type
Dissertation
Date of Award
2008
Abstract
Studies on body mass index (BMI) as it relates to headache or mortality have noted considerable nonlinearity. Approaches to rigorously analyzing these relationships have been limited to generalized linear models and survival models using either categorizations or polynomials of BMI. I have designed, evaluated, and implemented a piecewise linear logistic regression framework for modeling nonlinearity between a binary outcome and a continuous predictor, such as BMI, adjusted for covariates in complex samples. Least squares and maximum likelihood estimation methods were used to numerically optimize free-knot splines. Inference methods utilized both parametric and nonparametric bootstrapping. Parameter estimates were structured for interpretability by investigators familiar with logistic regression. Unlike other nonlinear software, this framework accounts for multistage cross-sectional survey sample designs. I applied this framework to complex datasets to examine the US population for headache among women and mortality as they respectively relate to BMI. For headache, datasets included the National Health Interview Survey (NHIS) and the first National Health and Nutrition Examination Survey (NHANES I). A common nadir in the BMIheadache relationship was detected around a BMI of 20, relative to which mild obesity (BMI of 30) and severe obesity (BMI of 40) were respectively associated with roughly 35% and 80% increased odds of headache. Mortality analyses focused on NHANES III. BMI showed a checkmark-shaped relationship with odds of mortality, but elevated BMI did not show significantly increased odds. This was unexpected and the product of a nascent analysis plan. Thus, this finding should be viewed as preliminary. Waist-to-hip ratio (WHR) has been used as an anthropometric predictor of mortality risk, but the shape of the relationship has not been carefully examined. For comparison with BMI, I investigated WHR in NHANES III. Linear logistic regression methods were sufficient for the WHR-mortality relationship, but WHR was a significant predictor for women only. The results of these studies relate broadly to the US population and the methods provide a flexible logistic regression framework for detecting and characterizing nonlinear relationships. The estimates may provide impetus for more focused obesity, headache, and mortality research which might realistically affect long-term public health policy and risk awareness.
Recommended Citation
Keith, Scott W., "Free -knot splines and bootstrapping for nonlinear modeling in complex samples" (2008). All ETDs from UAB. 6812.
https://digitalcommons.library.uab.edu/etd-collection/6812