Physical Sciences and Mathematics Commons^™

Statistical Analysis Of A Joint Stochastic Model For Recurrent Competing Risks, Longitudinal Marker, And Health Status, Lili Tong

Theses and Dissertations

Joint modeling approach has been applied in many applications in biomedical, reliability, and social-economic research. For example, in clinical trials and medical research, different kinds of patient information are gathered over time, such as recurrent competing risk events (e.g., relapses of different types of tumor), longitudinal marker (e.g., tumor size), and health status (e.g., if a patient is dead or not). These data are usually correlated, joint models enable the analysis of these correlated data. This dissertation proposes a class of joint dynamic models for simultaneously modeling the three types of processes: a recurrent competing risk (RCR) process, a health …

Statistical Methods With A Focus On Joint Outcome Modeling And On Methods For Fire Science, Da Zhong Xi

Electronic Thesis and Dissertation Repository

Understanding the dynamics of wildfires contributes significantly to the development of fire science. Challenges in the analysis of historical fire data include defining fire dynamics within existing statistical frameworks, modeling the duration and size of fires as joint outcomes, identifying the how fires are grouped into clusters of subpopulations, and assessing the effect of environmental variables in different modeling frameworks. We develop novel statistical methods to consider outcomes related to fire science jointly. These methods address these challenges by linking univariate models for separate outcomes through shared random effects, an approach referred to as joint modeling. Comparisons with existing …

Algorithms For Reconstruction Of Gene Regulatory Networks From High -Throughput Gene Expression Data, Wenping Deng

Dissertations, Master's Theses and Master's Reports

Understanding gene interactions in complex living systems is one of the central tasks in system biology. With the availability of microarray and RNA-Seq technologies, a multitude of gene expression datasets has been generated towards novel biological knowledge discovery through statistical analysis and reconstruction of gene regulatory networks (GRN). Reconstruction of GRNs can reveal the interrelationships among genes and identify the hierarchies of genes and hubs in networks. The new algorithms I developed in this dissertation are specifically focused on the reconstruction of GRNs with increased accuracy from microarray and RNA-Seq high-throughput gene expression data sets.

The first algorithm (Chapter 2) …

Joint Analysis Of Zero-Heavy Longitudinal Outcomes: Models And Comparison Of Study Designs, Erin R. Lundy

Electronic Thesis and Dissertation Repository

Understanding the patterns and mechanisms of the process of desistance from criminal activity is imperative for the development of effective sanctions and legal policy. Methodological challenges in the analysis of longitudinal criminal behaviour data include the need to develop methods for multivariate longitudinal discrete data, incorporating modulating exposure variables and several possible sources of zero-inflation. We develop new tools for zero-heavy joint outcome analysis which address these challenges and provide novel insights on processes related to offending patterns. Comparisons with existing approaches demonstrate the benefits of utilizing modeling frameworks which incorporate distinct sources of zeros. An additional concern in this …