Project Personnel (FDA, PCORI)
Dr. Daniel Scharfstein (Principal Investigator) is Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He has published widely on methods for sensitivity analysis in randomized studies with missing data. He was a member of the Committee of National Statistics expert panel, sponsored by the FDA, that produced the 2010 report entitled: "The Prevention and Treatment of Missing Data in Clinical Trials". He was also a co-investigator on a grant from PCORI that drafted standards for the prevention and handling of missing data in PCOR. Dr. Scharfstein has consulted with the FDA as a Special Government Employee. He and Dr. McDermott have implemented global sensitivity analyses for pharmaceutical companies seeking FDA approval of their products.
Dr. McDermott is an Associate Scientist of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He serves as the lead programmer on these grants. Dr. McDermott has been working as a software developer for over 25 years. He is a highly experienced in SAS, R and C, the programming languages that will be used for this project. He has worked for SAS Institute's European headquarters and regularly teaches SAS courses at Johns Hopkins. He has developed several R packages, built on C code, including ones for generalized estimating equations, generalized additive models and spatial-temporal modeling.
Dr. Chenguang Wang is an Asscoiate Professor, Department of Oncology, at Johns Hopkins University School of Medicine and a member of the Biostatistics Core for the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University. Dr. Wang has strong background in the field of computer science and experience in developing and applying a broad array of biostatistical methodologies to the design and analysis of clinical trials. Dr. Wang worked for many years as study statistician for Children’s Oncology group on children’s Acute Lymphoblastic Leukemia trials and as FDA mathematical statistical reviewer. Dr. Wang's statistical methodology interests involve Bayesian methods for missing data analysis and causal inference in clinical trial design and clinical data analysis.
Dr. Elizabeth Stuart is an Professor in the Departments of Mental Health and Biostatistics at the Johns Hopkins Bloomberg School of Public Health. She is an expert in the development of statistical methods for estimating causal effects and for handling missing data, and in the application of these methods to mental health, education, and public policy. She has published pedagogical papers on multiple imputation, propensity scores, sensitivity analyses to unobserved confounding, and complier average causal effect models. She helped develop the MatchIt propensity score package for R, which is a widely used tool. She also manages a highly accessed website providing links to propensity score software.
Dr. Tianjin Li is an Associate Professor in the Department of Ophthalmology at University of Colorado Denver with a secondary appointment in the Department of Epidemiology at University of Colorado School of Public Health. Previously, Dr. Li worked as an Associate Professor in the Department of Epidemiology at the Johns Hopkins Bloomberg School of Public Health where she was a core faculty member in the Center for Clinical Trials and Evidence Synthesis. The primary goal of Dr. Li’s research is to develop, evaluate, and disseminate efficient methods for comparing healthcare interventions and to provide trust-worthy evidence for decision-making. Her research has been funded by the National Institutes of Health, the Patient-Centered Outcomes Research Institute, the Agency for Healthcare Research and Quality, the U.S. Food and Drug Administration, and other national and international public funders.