Category: Topics

Efficient Estimation and Inference in Matrix Regression with Application to Neuroimaging Data and Bioassay Data

Matrix regression is a generalization of conventional multivariate regression. It has potential important applications in contemporary complex structured data, such as data from neuroimaging studies, cross-over design analysis, and multivariate growth curve modeling, among many others. In this talk, I will propose a new enveloping approach to reduce estimation standard errors in multivariate regression and […]

Using Hurdle Models to Analyze Zero-Inflated Count Data

Many outcomes in medical research are counts of some event. For example, heart rate is expressed as a count of beats per minute, or a patient may have more than one infection during an ICU stay, or a patient may be re-hospitalized a number of times following their index hospitalization. When the counts are large, […]

Meta-Analysis: How to Really Lie with Statistics

This presentation examines controversies surrounding the validity and meaningfulness of meta-analysis. Dr. Kolm is Director of Biostatistics at Christiana Care Health System, Research Professor of Medicine at Thomas Jefferson University and Director of the Biostatistical Core of the Bridging Advanced Developments for Exceptional Rehabilitation (BADER) Consortium funded by the Department of Defense. He has over […]

What if We Ignore the Random Effects When Analyzing RNA-seq Data in a Multifactor Experiment?

Identifying differentially expressed (DE) genes between different conditions is one of the main goals of RNA-seq data analysis. Although a large amount of RNA-seq data are produced for two-group comparison with small sample sizes at early stage, more and more RNA-seq data are being produced in the setting of complex experimental designs such as split-plot […]

Robust Estimation of Propensity Score in Observational Study

Observational Study aims to draw inference about the possible effect of a treatment on subjects from an empirical comparison of treated and controlled groups and is widely used in healthcare and medical research when clinical trials are not applicable in practice. The central issue in observational study is to identify potential confounding to ensure the […]

Time Series Analysis of 30-Day Readmission Rates: Health Care Innovation – Bridging the Divide

Application of times series methods to 30-day readmission rates of PCI patients in the BRIDGES database. Methods include interrupted time series, cross-correlation and Granger causality. Dr. Paul Kolm, DAC-CTR Associate Director, is Director of Biostatistics at Christiana Care Health System, and Research Professor of Medicine at Thomas Jefferson Medical College. He has been and is […]

Reducing Selection Bias Inverse Probability Weighting and Bin Bootstrapping

Reducing selection bias is a concern when analyzing observational datasets. IPW (inverse probability weighting) and PSBB (propensity score bin bootstrapping) are two methods used to address selection bias. This presentation will demonstrate how IPW and PSBB capture information from all patients with balance achieved for measured confounders via propensity score adjustment. Dr. Zugui Zhang is […]

Propensity Score Matching for Estimating Treatment Effects

Propensity Scores matching is used to adjust for selection bias in non-randomized studies to compare the effectiveness of interventions when there are significant baseline differences between the intervention groups. This presentation will discuss how to estimate the propensity score, form matched sets of subjects, assess the similarity of baseline factors between intervention groups, and estimate […]

Predictive Analytics Using Partial Least Squares Structural Equation Modeling

In recent years, Partial Least Squares Structural Equation Modeling (PLS-SEM) has emerged as a complementary SEM technique to analyze complex relationships among latent variables. Unlike traditional SEM, PLS-SEM is better suited for exploratory research where the goal is to best explain the observed data and/or make predictions about unobserved data (i.e., predictive analytics). Using a […]

Wilmington’s World Kidney Day Connects Community to Kidney Disease Screening Education and Research

Partners in Research, a community engagement effort spearheaded by the Value Institute, helped organize a local World Kidney Day event in Wilmington that offered community members free health screenings and education about kidney disease. The focus of this year’s event, hosted March 9 by Henrietta Johnson Medical Center in Wilmington, was the effect of obesity […]