Category: Innovative Discoveries Series

Can Big Data be Used for Comparative Effectiveness Research?

Data sets in medicine and health care are becoming larger and considerably more complex, meeting the definition of Big Data. The idea has been advanced that such data can be used to compare forms of therapy and inform medical decision making. While this is at times possible, there are considerable challenges in doing so. This […]

An Introduction to Comparative Effectiveness

Dr. Weintraub is Principal Investigator for the DE-CTR Accel Program at Christiana, and Director of the Clinical Research Design, Epidemiology, and Biostatistics Core. He is Professor of Medicine at Thomas Jefferson University and Adjunct Professor of Health Sciences at the University of Delaware. He received his MD from Johns Hopkins in 1975 and trained at […]

A Primer in Racial Ethnic Disparities in Healthcare and Outcomes

LeRoi S. Hicks, M.D., MPH is a doctor, researcher, and educator at Christiana Care Health System and serves at the vice chair of the Department of Medicine. Dr. Hicks graduated cum laude from Howard University in Washington D.C. with a B.S. in medical technology and obtained his medical degree at Indiana University. His postdoctoral training […]

RICH LIFE: Reducing Hypertension Disparities through Health System vs Multilevel Interventions

The RICH LIFE Study will help to lower blood pressure and heart disease risk among minority, low income, and rural populations, by comparing standard clinical performance feedback and education for providers and staff to a more comprehensive approach that includes workshops for health system leaders, a structured team approach to care, and access to subspecialists […]

Observational Study Overview and Design: An Instrumental Variable Based Approach

Observational Study aims to draw inference about the possible effect of a treatment on subjects from an empirical comparison of treated and controlled groups, which is widely used in all kinds of healthcare and medical research when clinical trials are not applicable in practice. In this presentation, I will focus on the overview of Observational […]

Piecewise Linear Mixed Effects Model in Tracking the Mean Response Trend

Modeling mean response over time and covariance among repeated measures on the same individuals are the main two aspects of longitudinal data analysis. Linear mixed effects models track the mean response as a combination of population characteristics that are assumed to be shared by all individuals and subject-specific effects that are unique to a particular […]

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 […]