Billie S Anderson

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Most discoveries of cancer biomarkers involve construction of a single model to determine predictions of survival.. 'Data-mining' techniques, such as artificial neural networks (ANNs), perform better than traditional methods, such as logistic regression. In this study, the quality of multiple predictive models built on a molecular data set for colorectal(More)
BACKGROUND Although a majority of studies in cancer biomarker discovery claim to use proportional hazards regression (PHREG) to the study the ability of a biomarker to predict survival, few studies use the predicted probabilities obtained from the model to test the quality of the model. In this paper, we compared the quality of predictions by a PHREG model(More)
Ongoing processes account for a large fraction of the brain activity observed in functional imaging. The study of this intrinsic, or spontaneous, activity is currently a major field of research in neuroscience, as it opens the door to fundamental insights on brain architecture and can be applied to severely impaired patients for the study of pathologies.(More)
This article explores the statistical methodologies used in demonstration and effectiveness studies when the treatments are applied across multiple settings. The importance of evaluating and how to evaluate these types of studies are discussed. As an alternative to standard methodology, the authors of this article offer an empirical binomial hierarchical(More)
We compared the accuracy of 3 data-mining models, neural-network, decision-tree, and logistic-regression, in predicting the 5-year survival of patients with colorectal cancer. The database consisted of patient demographics, pathologic features, and levels of expression of 2 biomarkers (p53 and Bcl-2). All 3 methods demonstrated acceptable accuracy, from 64%(More)
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