Innovative statistical methods for biomedical and clinical applications
Creators: name: Duan, Yajie
Publisher: name: No Publisher Supplied
Year: 2024

description: This thesis introduced novel statistical models and methods developed to address difficulties in analyzing data from cutting-edge biomedical and clinical applications. It addresses significant challenges in big data visualization, regression analysis between variables with errors, precision decision-making, and accurate quantitative estimation, with a focus on three key biomedical and clinical applications. Chapter 1 proposed a novel approach for visualizing and analyzing extremely large datasets using Projection Pursuit, Grand and Guided Tours, and Data Nuggets methods. An important application is analyzing vast datasets from cell flow cytometry experiments, to help investigations into surface protein functions of cells. The proposed methodology, along with a new Projection Pursuit index that can be easily computable for big data, enables the discovery of hidden structures such as clusters, outliers, and nonlinear patterns within extensive datasets. An R package, emph{PPbigdata}, was developed and published on CRAN to provide a powerful tool for performing 1-dim/2-dim projection pursuit for big data based on data nuggets. Chapters 2 and 3 focused on developing innovative regression frameworks and decision-making methods with clinical applications to personalized treatment recommendations. Chapter 2 proposed a two-stage Deming regression framework to accurately analyze associations between variables observed with errors of known or estimated variances. This framework is particularly useful in balancing clinical risks, but its versatility also makes it applicable to handling privacy-protected data containing multiple sources of uncertainty, such as in differential privacy scenarios. Chapter 3 introduced a precision decision-making method that quantitatively combines evidence-based knowledge with subjective preferences using prediction intervals to optimize decisions. A significant application of this method is in patient-centered treatment recommendations. Additionally, it can aid decision-making in other fields such as economics and demography. These methods were demonstrated through a case study of patients diagnosed with atrial fibrillation, providing patient-centered guidance for anticoagulant medication decisions by balancing the risks of stroke and bleeding. Chapters 4 and 5 addressed challenges in serial dilution experiments, which are widely used in immunology, virology, and other biomedical and pharmaceutical experiments. Chapter 4 proposed a new model based on joint likelihood to estimate the concentration of microorganisms in neat samples by modeling count data from the entire single dilution series, offering increased precision and accuracy compared to existing methods. Chapter 5 presented a complete workflow for estimating microorganism concentrations by enhancing automatic spot counting in images with spots representing viral plaque forming units (PFU), bacterial colony forming units (CFU), or spot forming units (SFU). The accuracy of the methods was significantly improved by introducing both empirical and theoretical bias correction techniques to address undercounting issues that arise when spots are densely populated. Overall, the proposed innovative statistical models and methods provide new tools for big data visualization, regression analysis, decision-making, and quantitative estimation, with applications extending beyond the medical and clinical fields to areas such as differential privacy, economics, and demography. This thesis advances data analysis techniques by developing novel statistical approaches specifically tailored to biomedical and clinical applications, while also offering generalizable tools for broader use.