Project Personnel

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 (Co-Investigator) is an Associate Scientist of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He will serve as the lead programmer on this project. 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 Assistant 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 (Co-Investigator) is an Associate 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 (more than 800 times per month) website providing links to propensity score software (

Dr. Tianjin Li is an Assistant Professor of Epidemiology at the Johns Hopkins Bloomberg School of Public Health. She brings a different perspective to our team. Dr. Li’s core research interests lie in methodology for and conduct of randomized controlled trials, comparative effectiveness research (CER), systematic reviews, meta-analysis, missing data, and reporting biases. She oversees and consults on several projects on prioritizing CER where consensus opinions from a large group of stakeholders are sought. She serves as Project Director for an NIH Challenge Grant focusing on methodology related to network meta-analysis examining comparative effectiveness of multiple medical interventions for glaucoma, where she frequently deals with missing data problems. For the past seven years, she has served as a senior methodologist with the Cochrane Collaboration where she has published a number of important systematic reviews and meta-analyses. She has also served as the Associate Director of the Coordinating Center for an NIH-funded randomized controlled trial. Dr. Li co-convenes the Cochrane Comparison of Multiple Interventions Methods Group. Dr. Li served as the Principal Investigator on a PCORI contract that recommended methodological standards in the prevention and handling of missing data. Her knowledge and experience in designing and performing PCOR and CER will enable her to provide a user’s perspective in designing the software. With her broad perspective and expertise, Dr. Li will contribute to disseminating the software to future PCOR researchers through preparing open-access training materials, presentations, and publications.