The Bayesian Statistical Science Section of the ASA provides a forum for statisticians and people who have interest in the Bayesian paradigm. The broad objectives of the Section are: to encourage research on theory and methods of statistical inference and decision making associated with Bayes' theorem and to encourage the application and proper use of Bayesian procedures in the behavioral, biological, managerial, engineering, environmental, legal, medical, pharmaceutical, physical, and social sciences.
2016 SBSS Student Paper Competition
The Section on Bayesian Statistical Science (SBSS) of the American Statistical Association (ASA) is pleased to sponsor a student paper competition for research on Bayesian methodology, which may be broadly construed and includes applied, computational, or theoretical work. This paper competition is for completed research. A manuscript suitable for journal submission is required to enter the competition. Winners of the competition will receive partial support to attend the 2016 Joint Statistical Meetings (JSM) in Chicago, Illinois.
The deadline for application is Dec. 15, 2015. (More Info)
Upcoming Web-Based Lectures
Title: Clinical Trial Designs for Validating Prognostic and Predictive Markers in Oncology
Presenter: Daniel J. Sargent, Mayo Clinic
Date and Time: Tuesday, December 8, 2015, 12:00 p.m. - 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section and Section on Medical Devices and Diagnostics
Registration Deadline: Friday, December 4, at 12:00 p.m. Eastern time
Increasing scientific knowledge is creating both substantial opportunities and challenges in oncology drug development. As diseases are often sub-stratified into biomarker-based groups, usual paradigms for phase II and III trials may no longer apply. In some circumstances, a carefully conducted retrospective-prospective analysis may provide sufficient evidence of a predictive biomarker for clinical use. Prospectively planned, enrichment designs are appropriate when preliminary evidence suggests that patients with/without that marker profile do not benefit from treatments in question; however this may leave questions unanswered regarding the activity of an agent in a larger but still relevant population. An unselected design is optimal where preliminary evidence regarding treatment benefit and assay reproducibility is uncertain. The biomarker-based strategy design may be useful when there is a choice between many treatment options. Adaptive analysis designs allow for pre-specified data-driven marker-defined subgroup analyses from a RCT. Umbrella or basket trials enroll large groups of patients with subsequent assignment to either individual randomized trials or single arm investigations. These trials may be disease specific, or may include patients from multiple sites who share a common biomarker status. We discuss features of these various novel design strategies in the context of multiple ongoing and planned real trials. Emphasis will be placed on practical considerations that may impact an academically optimal design.
*** More information and registration at the webinar page.