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Management Science and Statistics News

Seminar Series: Fall 2015
The seminar series will usually take place on Fridays in a Business Building room, but the exact time and location could be different due to a variety of factors including room availability in the Business Building. For actual direction to the Business Building, please see campus map. For additional information contact Dr. Kefeng Xu, (210) 458-5388.


Tuesday, August 25th, 2015 at 2-3pm, Business Building 4.02.10 (Executive Conference Room)

  • Presenter: Dr. Joel Michalek Professor and Vice-Chair Department of Epidemiology & Biostatistics in UTHSCSA

  • Presentation Title: An Introduction to Diagnosis and Statistical Classification

  • Abstract: Fundamental concepts pertaining to statistical classification and prediction and associated measures of accuracy will be illustrated with data from a recently published randomized controlled clinical trial of real-time detection of neoplastic tissue in Barrett's esophagus with probe-based confocal laser endomicroscopy. The results of the trial were generally poor and the overall goal, to demonstrate sufficient accuracy of laser endomicroscopy to eliminate the need for invasive biopsy, was not achieved.


Friday, October 16th, 2015 at 2-3pm, Business Building 4.02.10 (Executive Conference Room)

  • Presenter: Dr. Jon Gelfond, Associate Professor of the Department of Epidemiology & Biostatistics in UTHSCSA

  • Presentation Title: Beyond Reproducibility - a Framework for an Accountable Data Analysis Process (ADAP)

  • Abstract: Data analysis is essential to the scientific process, and although recent advances in promoting reproducibility and reporting standards have made some improvements, the data analysis process remains insufficiently documented and susceptible to unintentional errors, bias, and even fraud. A shortcoming of computational reproducibility is that it fails to consider the human interactions within the research team that can affect the analytical process. Comprehensively accounting for the full process of analysis requires not only records of the data flow, but also records of communications among the research team. We propose a novel framework for capturing this analytical narrative called the Accountable Data Analysis Process (ADAP). The framework consists of data structures that characterize communications and that sustain accountability, which most current analytical practices lack. We discuss the design, advantages, disadvantages, and challenges in implementing this type of system in the context of team science in such fields as clinical and translational research but the approach is generalizable to multiple scientific disciplines.




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