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 the effect of treatment in matched samples.

Dr. Zugui Zhang is the lead Biostatistician for the Value Institute of Christiana Care Health System and research assistant professor at Thomas Jefferson University. He has a broad background in health outcomes research, public health, epidemiology, and biostatistics. He has worked extensively on large international and national, multi-center, randomized clinical trials and observational studies.