Professor Emeritus, University of Florida
Talk Title: The Rising of Academic Statistics and Biostatistics, with Focus on New England.
Abstract: Xiao-Li Meng and I recently edited a book — `Strength in Numbers: The Rising of Academic Statistics Departments in the U. S.’ — in which the 40 U.S. Statistics and Biostatistics departments that have been in existence since at least the mid-1960s each submitted a memoir describing key aspects of the department’s history. These memoirs focused on each department’s founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, and a summary of where the department stands today and its vision for the future. My presentation will summarize some of the development, illustrating with a success story and a failure story and an overview of the development of four departments in New England.
Bio: Alan Agresti is Distinguished Professor Emeritus of Statistics at the University of Florida. He is author or co-author of more than 100 articles and six books, including “Categorical Data Analysis” (3rd ed. 2013) and “Statistics: The Art and Science of Learning from Data” (3rd ed. 2011). A fellow of the ASA and of IMS, Agresti has received an honorary doctorate from De Montfort University in the U.K., the Statistician of the Year award from the Chicago chapter of ASA, and the first Herman Callaert Leadership Award in Biostatistical Education and Dissemination from Hasselt University, Belgium. Since 2007 he has lived part of each year in Boston. In fall terms 2008-2013, he was a visiting professor at the Statistics Department of Harvard.
Professor of Biostatistics, Harvard School of Public Health
Talk Title: Moving Beyond the Comfort Zone in Practicing Translational Statistics.
Abstract: Over the years, the process of designing, monitoring, and analyzing clinical studies for evaluating new treatments has gradually fallen into a fixed pattern. Practitioners have been slow to utilize novel methodologies–perhaps to avoid potential delays in the review process. Scientific investigation is an evolving process. What we have learned about methodological shortcomings in previous studies should help us better plan and analyze future studies. In this talk, we will explore various methodological issues and potential solutions to them. More importantly, we will discuss how to improve the current practice to speed the development of medicines by fostering the development of reliable, clinically meaningful conclusions from the patient’s risk-benefit perspectives.
Bio: L.J. Wei’s research is in the area of developing statistical methods for the design and analysis of clinical trials. In 1977-78 he introduced the “urn design” for two-arm sequential clinical studies. This design has been utilized in several large-scaled multi-center trials, for example, the Diabetes Control and Complications Trial sponsored by the NIH and the Matching patients to Alcoholism Treatments sponsored by NIAAA.
In 1979, he proposed a response adaptive design, a randomized version of Marvin Zelen’s play the winner rule, was used in the ECMO trial, a well-known study which evaluated extracorporeal membrane oxygenation for treating newborns with persistent pulmonary hypertension. Currently several trials sponsored by private industry are using this particular design to relax the ethical problem arising in using the conventional 50-50 randomization treatment allocation rule clinical studies. To monitor trials sequentially for economic and ethical reasons, in 1982 Wei and his colleagues presented a rather flexible monitoring scheme, which has become a classical reference for the literature in interim analysis for clinical trials.
Dr. Wei has developed numerous methods for analyzing data with multiple outcome or repeated measurements obtained from study subjects. In particular, his “multivariate Cox procedures” to handle multiple event times have become quite popular. He and his colleagues are also responsible for developing alternative models to the Cox proportional hazards model for analyzing survival observations.
A very important issue in statistical inference is to check whether the model used to fit the data is appropriate or not. Currently, Wei and his colleagues are developing graphical and numerical methods for checking the adequacy of the Cox proportional hazards model, other semi-parametric survival models, parametric models, and random effects models for repeated measurements. The new procedures are much less subjective than the conventional eye-ball methods based on ordinary residuals plots.
Since the cost of computing has been drastically reduced, some analytically intractable statistical problems can be handled numerically. Presently, Wei and his colleagues are working on various resampling methods for quantile regression, rank regression, and regression models for censored data.
Dr. Wei is also a senior statistician at the Statistical and Data Analysis Center. He works closely with the medical investigators in Pediatrics AIDS clinical trials for evaluating new treatments for HIV patients.