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Home / News & Events / Safety Data Analysis and Reporting

Safety Data Analysis and Reporting: Signal Detection, Data-Mining and Next Generation Methodology for Drug Risk Assessment and Safety Research

Presented: September 29th, 2005

Speakers: Alejandro Murua, University of Montreal and Michael O'Connell, Insightful Corporation

Listen to the archived Web cast.

Abstract: In its efforts to continue to serve the public health and protect public safety, the pharmaceutical industry is now challenged to set up sound pharmacovigilance plans that carefully analyze and report on pre-marketing clinical study data, minimize risk and monitor post-marketing safety. These plans include the statistical analysis and reporting of adverse events in clinical studies and the proactive analysis of observational data such as FDA AERS for signal detection and risk management. In this context, the use of sophisticated statistical analysis models and data mining techniques is of increasing importance in pharmacovigilance, as these methods are able to detect signals earlier and more accurately than current methods. This presentation includes a review of statistical analysis, reporting and data mining of clinical/safety data by way of several case studies involving clinical trial and AERS data. The case studies include statistical modeling methods such as hierarchical Bayesian models, neural nets, tree ensembles and logistic regression. The data analyses and reporting examples feature the use of
S-PLUS, a highly flexible statistical workbench environment that enables advanced statistical analysis and reporting including interactive patient/subject profiling and standardized reporting.

Presenter Information

Alejandro Murua, University of Montreal

 

Alejandro Murua received his PhD degree in applied Mathematics from Brown University in 1994. He has been in the faculty of the Division of Applied Mathematics at Brown University, the Department of Statistics of the University of Chicago, and the Department of Statistics of the University of Washington. Currently he is an associate professor of the Department of Mathematics and Statistics
of the University of Montreal. His main research interests focus on applications of statistics and probability to machine learning, object recognition, bioinformatics, signal processing and data mining. He has been a member of the Program Committee of the 2004 and 2005 KDD Conferences. He is
an associate editor of the International Journal of Tomography and Statistics (IJTS).

Michael O'Connell, Ph.D., Insightful Corporation

 

Michael O'Connell, Ph.D., is director of Life Science Solutions at Insightful Corp. He has more than 15 years experience in the medical device, informatics and health-care statistics arena, having published more than 50 papers on statistics, data mining and health-care applications. This has included statistical methods work in the areas of non-parametric regression, experimental design, calibration and mixed models; and applications such as DNA amplification, diagnostics, microarray data analysis and safety data mining.