Events

Abstract: With the rapidly increasing availability of data in the public domain, combining information from different sources to infer about associations or differences of interest has become an emerging challenge to researchers. We present a novel approach to improve efficiency in estimating the survival time distribution by synthesizing information from the individual-level data with t-year […]

Abstract: Although increasingly used as a data resource for assembling cohorts, electronic health records (EHRs) pose many analytic challenges because they are primarily collected for clinical encounters rather than for research purposes. In particular, a patient's health status influences when and what data are recorded, generating sampling bias in the collected data. In this paper, […]

Abstract: Frequently primary analyses in clinical trials of progressive diseases use change-from-baseline type analyses, such as the MMRM.  These analyses of the absolute changes at different time points ignores that the disease is progressive, and the mechanism of the intervention is to slow progression. In this talk I’ll present several example in which a Bayesian […]

Abstract: Spacings, which are the gaps between successive observations, have been utilized in statistical inference both for estimation and in testing of hypotheses. After a brief review of this area, we introduce estimators based on higher-order or multi-step spacings, called the “Generalized Spacings Estimators (GSEs)”. Such estimators are obtained by minimizing the so-called Csiszar divergence […]

Abstract: Single-cell RNA sequencing (scRNA-seq) technology provides higher resolution of gene expression to study the cellular level expression heterogeneity in different tissues. However, one major challenge in scRNA-seq data analysis is the low capture efficiency that results in a large proportion of zero in the data matrix. For genes with low or moderate expression, this […]

Abstract: Lesions in the white matter of the brain, including those that arise in multiple sclerosis, are abnormalities measurable on MRI. While much literature has focused on the identification of these lesions, less work has focused on the nature of these lesions. As new imaging modalities arise that allow us to better interrogate these lesions, […]

Abstract: While magnetic resonance imaging (MRI) studies are critical for the diagnosis, monitoring, and study for a wide variety of diseases, their use in quantitative analysis can be complex. An increasingly recognized issue involves the differences between MRI scanners that are used in large multi-center studies. To address this, the current state of the art […]

Abstract: High-dimensional point processes have become ubiquitous in many scientific fields. For instance, neuroscientists use calcium florescent imaging to monitor the firing of thousands of neurons in live animals. In this talk, I will discuss new methodological, computational and theoretical developments for learning neuronal connectivity networks from high-dimensional point processes. Time permitting, I will also […]

Abstract: Cancer 'omics datasets involve widely varying sizes and scales, measurement variables, and correlation structures. An overarching scientific goal in cancer research is the development of general statistical techniques that can cleanly sift the signal from the noise in identifying genomic signatures of the disease across a set of experimental or biological conditions. We propose […]