Department of Biostatistics
Statistical Methods in Epidemiology Working Group
2014 - 2015
ABSTRACT: Serum albumin is a leading index of protein-energy malnutrition (PEM) that has been associated with mortality among hemodialysis patients. Studies have found that albumin levels at the start of dialysis and the slope of albumin over time are independent risk factors for mortality. It is also natural to hypothesize that high within-subject variability in albumin measured over time may also be indicative of increased mortality. That is, high instability around a patient's first-order trend is likely an indication of nutritional instability and hence may be a risk factor for morbidity and mortality. We develop a Gaussian process model for estimating a summary measure of within-subject volatility in serum albumin measured over time. The proposed model includes a parameter to allow for subject-to-subject variability and places a Dirichlet process prior on the unknown distribution from which these subject-specific parameters are drawn in order to cluster subjects with similar longitudinal patterns without specifying the number of clusters. Simulation studies that assess the proposed model are presented and an illustrative example is provided where the induced summary measure of within-subject volatility is associated with mortality using patients from the United States Renal Data System.
ABSTRACT: In nutritional epidemiology studies of diet and health often focus separately on single dietary nutrients or foods as they pertain to disease risk. As the bioactive component of foods, nutrients are believed to be the causal compounds related to disease. However, the entirety of health effects of foods is rarely captured by considering a single nutrient. Additionally, considering the effects of a food is difficult as they aren't consumed in isolation. Here we present an alternative approach allowing for a more comprehensive analysis of multiple foods on the risk of disease using a two-stage regression approach. The proposed hierarchical model uses nutrient-level information to "shrink" or pull ordinary estimates of food effects towards or away from one another when a food has a similar nutrient composition. We present results using these models to assess the effects of many different foods on the risk of diabetes or cardiovascular disease using data from the Nurses' Health Study. Research by Kathryn Fitzgerald, Bernard Rosner and Karin Michels.
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