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Imbalanced data, a common challenge encountered in statistical analyses of clinical trial datasets and disease modeling, refers to the scenario where one class significantly outnumbers the other in a binary classification problem. This imbalance can lead to biased model performance, favoring the majority class, and affecting the understanding of the relative importance of predictive variables. Despite its prevalence, the existing literature lacks comprehensive studies that elucidate methodologies to handle imbalanced data effectively. In this study, we discuss the binary logistic model and its limitations when dealing with imbalanced data, as model performance tends to be biased towards the majority class. We propose a novel approach to addressing imbalanced data and apply it to publicly available data from the VITAL trial, a large-scale clinical trial that examines the effects of vitamin D and Omega-3 fatty acid to investigate the relationship between vitamin D and cancer incidence in sub-populations based on race/ethnicity and demographic factors such as body mass index (BMI), age, and sex. Our results demonstrate a significant improvement in model performance after our undersampling method is applied to the data set with respect to cancer incidence prediction. Both epidemiological and laboratory studies have suggested that vitamin D may lower the occurrence and death rate of cancer, but inconsistent and conflicting findings have been reported due to the difficulty of conducting large-scale clinical trials. We also utilize logistic regression within each ethnic sub-population to determine the impact of demographic factors on cancer incidence, with a particular focus on the role of vitamin D. This study provides a framework for using classification models to understand relative variable importance when dealing with imbalanced data.
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This content will become publicly available on August 18, 2026
Transmission patterns that reduce population size without increasing disease prevalence: masting and chronic wasting disease in white-tailed deer
Abstract How individuals use space and, thus, the rate and the nature of their interactions with others are shaped by their environment. Exogenous changes that alter aggregation patterns, such as resource pulses, can therefore have a significant impact on seemingly unrelated processes like disease spread. White-tailed deer (Odocoileus virginianus) aggregate in oak forests during mast events, and chronic wasting disease (CWD) transmission patterns vary with deer density, so we hypothesize a link between the masting cycle and CWD dynamics. We investigate various possible effects of masting on deer, including shifts to more frequency-dependent CWD transmission due to aggregation, as well as elevated fecundity and decreased mortality of deer in response to the resource pulse, using a simplified compartment model of CWD spread. When masting affects epidemiological parameters, including the strength of frequency dependence in CWD transmission, disease spread during masting events significantly reduces the size of deer populations but, paradoxically, without any change in the proportion of the population in the CWD-diseased state. In contrast, demographic parameters were found in principle to be capable of altering both population size and disease incidence, though the observed effects were very small. While our quantitative findings should be validated using more detailed models of CWD transmission before they are taken as specific predictions about this system, our fundamental qualitative result appears to be quite general. That is, our conclusion that epidemiological rates only influence population size, but demographic rates may affect both population size and disease incidence, can be derived not only from the model we studied but also from classical epidemiological models as well. Our work extends the understanding of the far-reaching impacts of resource pulses through ecological communities by highlighting the vastly different consequences of the same resource pulse acting in different ways.
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- Award ID(s):
- 2325078
- PAR ID:
- 10635220
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Theoretical Ecology
- Volume:
- 18
- Issue:
- 1
- ISSN:
- 1874-1738
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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