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The Information Systems and Statistics Research Seminar Series
From: Prof. Qiang (David) Gao, Paul H. Chook Department of Information Systems and Statistics
Alexia Iasonos, Ph.D
Date: Oct 23, 2018
Time: 12:30 to 1:45PM
Location: 11-217 IS-STA Conference room
Title: Phase I designs that allow for uncertainty in the attribution of adverse events
Abstract: In determining dose limiting toxicities in Phase I studies, it is necessary to attribute adverse events (AE) to being drug related or not. Such determination is subjective and may introduce bias. We developed methods for removing or at least diminishing the impact of this bias on the estimation of the maximum tolerated dose (MTD). The proposed approach takes into account the subjectivity in the attribution of AE by using model-based dose escalation designs. It allows the investigators to strictly adhere to the protocol by recording all dose limiting toxicities and, yet, still allow expert opinion to prevent AE that are most likely not drug related from seriously compromising the exercise of identifying the correct MTD. The results show that gains can be achieved in terms of accuracy by recovering information lost to biases. These biases are a result of ignoring the errors in toxicity attribution. Theoretical results and simulation studies under small sample sizes will be presented.
Biography: Dr. Iasonos has been at MSKCC since early 2005 after working at Bristol Myers Squibb for 3 years. She has collaborated primarily with investigators studying ovarian cancer and also with investigative teams studying bladder cancer, lymphoma, and health outcomes. Through her collaborations with investigators in gynecology (Departments of Surgery, Medicine and Pathology) she is exploring various biomarkers and assessing relationships to histology, recurrence and clinical outcome. She is also involved in vaccine trials as a second line therapy in ovarian cancer patients, and in identifying valid endpoints for these trials. Her methodological interests focus on model based designs that guide the dose escalation in phase I trials and in the past few years she has focused on the design of early phase trials and basket trials that involve dose expansion cohorts. Alexia previously served as a member of the institutional Data Safety Monitoring Committee for Phase I-II trials and is currently a member of Research Council which is the Protocol Review and Monitoring System for the Cancer Center Support Grant that reviews protocols for scientific merit, priority, and progress. She is an Associate Editor for the Journal of Clinical Oncology and Clinical Trials.
Prof. Suprateek Sarker
Rolls-Royce Commonwealth Commerce Professor McIntire School of Commerce
University of Virginia
Date: November 1, 2018
Time: 11:05 AM to 12:10PM
Location: 11-217 IS-STA Conference room
Title: Researching Work-Life Conflict (WLC) of IT Professionals:
Toward a Humanistic & Engaged Research Program
Abstract: In recent times, scholarly as well as practitioner articles have recognized the severe Work-Life Conflict (WLC) IT professionals routinely experience in their work, and the negative consequences of WLC. Our review of the related literature, however, reveals that not much is known about the sources of WLC arising uniquely from the distributed nature of IT professionals' work as well as the specific characteristics of IS development. In this presentation, I will discuss the evolution of a research program on WLC that is tailored toward IS professionals and knowledge workers involved in distributed work. I will also discuss specific findings from one large mixed-method study and two smaller qualitative studies that will hopefully open up the area and get some of the members of the audience interested in the topic.
Biography: Suprateek Sarker (“Supra”) is Rolls-Royce Commonwealth Commerce Professor (information technology) at the McIntire School of Commerce, University of Virginia. His research, which is largely qualitative in nature, has been published in leading journals. He serves or has served on a number of editorial boards, including MIS Quarterly (as former senior editor), Journal of Management Information Systems (on the board of editors), Decision Sciences Journal (as former senior editor) Information Technology & People, IEEE Transactions on Engineering Management, Journal of Information Technology Case and Application Research (as former editor in chief) and the Journal of the Association for Information Systems (as the current editor in chief). In 2006, he was a co-recipient of the Stafford Beer Medal from the Operational Research Society (United Kingdom); in 2016, he was awarded an honorary doctorate by the Faculty of Information Technology, University of Jyväskylä (Finland); in 2017, he was named a fellow of the Association for Information Systems; and in April 2018, he was named distinguished alumnus of Operations, Business Analytics, and Information Systems, University of Cincinnati, where he completed his PhD studies in 1997.
Prof. Xiao Fang
Professor of MIS
Alfred Lerner College of Business and Economics
University of Delaware
Date: November 8, 2018
Time: 11:05 AM to 12:20PM
Location: 11-217 IS-STA Conference room
Title: Social Network Analytics: Adoption, Persuasion, and Link Recommendation
Abstract: Social networks such as those facilitated by social media, online games, or mobile devices have attracted increasing attention from both academia and industry that explore how to leverage such networks for greater business and societal benefits. Toward that end, we develop novel models, theories, and methods that mine massive social network data for business purposes. In this project, we focus on a unique phenomenon in social networks – the diffusion of adoption behavior (e.g., adoption of a product, service, or opinion) from one social entity to another. Specifically, we investigate three critical and related problems concerning this phenomenon: adoption, persuasion, and link recommendation. That is, the diffusion of adoption behavior is initiated by persuaders and reached to adopters through the linkage structure of a social network. Accordingly, we study the following problems: how to predict adoption probabilities in a social network? how to predict top persuaders in a social network? and how to recommend links for a social network? Let us take the problem of predicting adoption probabilities as an illustration. Adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Building on relevant social network theories, we identify key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and hidden factors. The principal challenge thus is how to predict adoption probabilities in the presence of hidden factors that are generally unobserved. To address this challenge, we develop a Bayesian learning method on the basis of the expectation-maximization framework. Using data from two large-scale social networks, we demonstrate that the developed method significantly outperforms prevalent existing methods. The empirical results also offer two interesting observations: existing methods that exclusively use social influence to predict adoption probabilities seem ineffective; hidden factors appear to play a significant role in adoption probability predictions.
Biography: Xiao Fang is Professor of Management Information Systems and JPMorgan Chase Fellow at Lerner College of Business & Economics and Institute for Financial Services Analytics, University of Delaware. He also holds courtesy appointments at Departments of Computer Science and Electrical Engineering, University of Delaware. He studies business and social network analytics with research methods and tools drawn from reference disciplines including Management Science (e.g., Optimization) and Computer Science (e.g., Machine Learning). He has published in business journals including Management Science, MIS Quarterly, and Information Systems Research, Operations Research, as well as computer science outlets such as ACM Transactions on Information Systems and IEEE Transactions on Knowledge and Data Engineering. Professor Fang currently serves as Associate Editor for MIS Quarterly.
Presented by the Paul H. Chook Department of Information Systems and Statistics
If you have any questions, please contact Qiang Gao at Qiang.Gao@baruch.cuny.edu.
Qiang (David) Gao
Paul H. Chook Department of Information Systems and Statistics
Baruch College, The City University of New York
Phone: (646) 312 3192