Dynamic Prediction of Acute Graft-versus-Host-Disease with Longitudinal Biomarkers

This dissertation builds three prediction tools to dynamically predict the onset of acute graft-versus-host disease (aGVHD) with longitudinal biomarkers. Acute graft-versus-host disease is a complication for patients who have received allogeneic bone marrow transplant, and it is fatal for some patients. Clinicians could benefit from these prediction tools to identify patients who are at risk and who are not, and thus assign appropriate interventions. Our first project introduces how to apply joint modeling with latent classes (JMLC) and landmark analysis to aGVHD data. In JMLC, we group all aGVHD-free patients into one latent class and define that class as the "cure" class. In landmark analysis, we incorporate patients' biomarker information up to the landmark time to gain more efficiency. Computer simulations show that both methods adjust for the measurement error, and that JMLC outperforms landmark analysis when the functional form of the biomarker profile is correctly specified. In our second project, we describe how to execute dynamic prediction with the pattern mixture model, in which each patient is classified by his/her time-to-aGVHD, and patients in the same group share the same mean profile of biomarkers. The pattern mixture model is easy to execute and straightforward to interpret. Simulations indicate that the pattern mixture model controls loss of accuracy in predictions. In our third project, we incorporate censored cases to generalize the pattern mixture model in the second project. The simulation results demonstrate that this generalized pattern mixture model accurately estimates of the marginal pattern probabilities, and thus better estimates early predictions compared to early predictions not incorporating censored observations. In our fourth project, we explain the process of parametric bootstrap in selecting the number of latent classes in JMLC. Compared with the standard information-based criteria in model selection in JMLC, our parametric bootstrap likelihood ratio test (LRT) controls the Type I error well while maintaining sufficient power. We also propose two sequential early stopping rules to relieve the computational burden of bootstrap.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144104/1/yumeng_1.pd

China's New Third Board Market: Opportunities and Challenges

AbstractThe New Third Board Market is China's OTC market, established in 2006. Compared to China's Main Board Market and the Second Board Market, it attracts a lot of start-up companies needing financing with lower listing requirements. Meanwhile, it is full of opportunities and challenges that appeal to numerous securities traders and investors with the rapid development momentum. This paper is intended to build a comprehensive and systematic knowledge framework of China's New Third Board Market for those enterprises and individuals interested in it, and to provide a research base for future researchers

Channel Estimation for Beyond Diagonal Reconfigurable Intelligent Surfaces with Group-Connected Architectures

We study channel estimation for a beyond diagonal reconfigurable intelligent surface (BD-RIS) aided multiple input single output system. We first describe the channel estimation strategy based on the least square (LS) method, derive the mean square error (MSE) of the LS estimator, and formulate the BD-RIS design problem that minimizes the estimation MSE with unique constraints induced by group-connected architectures of BD-RIS. Then, we propose an efficient BD-RIS design which theoretically guarantees to achieve the MSE lower bound. Finally, we provide simulation results to verify the effectiveness of the proposed channel estimation scheme.Comment: 5 pages, 2 figures, accepted by CAMSAP 202