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Full-Text Articles in Medicine and Health Sciences
Evaluation Of The Performance Of Smoothing Functions In Generalized Additive Models For Spatial Variation In Disease, Umaporn Siangphoe, David C. Wheeler
Evaluation Of The Performance Of Smoothing Functions In Generalized Additive Models For Spatial Variation In Disease, Umaporn Siangphoe, David C. Wheeler
Biostatistics Publications
Generalized additive models (GAMs) with bivariate smoothing functions have been applied to estimate spatial variation in risk for many types of cancers. Only a handful of studies have evaluated the performance of smoothing functions applied in GAMs with regard to different geographical areas of elevated risk and different risk levels. This study evaluates the ability of different smoothing functions to detect overall spatial variation of risk and elevated risk in diverse geographical areas at various risk levels using a simulation study. We created five scenarios with different true risk area shapes (circle, triangle, linear) in a square study region. We …
Penalized Ordinal Regression Methods For Predicting Stage Of Cancer In High-Dimensional Covariate Spaces, Amanda Elswick Gentry, Colleen K. Jackson-Cook, Debra E. Lyon, Kellie J. Archer
Penalized Ordinal Regression Methods For Predicting Stage Of Cancer In High-Dimensional Covariate Spaces, Amanda Elswick Gentry, Colleen K. Jackson-Cook, Debra E. Lyon, Kellie J. Archer
Biostatistics Publications
The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, …
Catchment Area Analysis Using Bayesian Regression Modeling, Aobo Wang, David C. Wheeler
Catchment Area Analysis Using Bayesian Regression Modeling, Aobo Wang, David C. Wheeler
Biostatistics Publications
A catchment area (CA) is the geographic area and population from which a cancer center draws patients. Defining a CA allows a cancer center to describe its primary patient population and assess how well it meets the needs of cancer patients within the CA. A CA definition is required for cancer centers applying for National Cancer Institute (NCI)-designated cancer center status. In this research, we constructed both diagnosis and diagnosis/treatment CAs for the Massey Cancer Center (MCC) at Virginia Commonwealth University. We constructed diagnosis CAs for all cancers based on Virginia state cancer registry data and Bayesian hierarchical logistic regression …