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Home / News & Events / Enhanced Clinical Data Analysis & Reporting Using S-PLUS 6.2

Enhanced Clinical Data Analysis & Reporting Using S-PLUS 6.2

Presented: November 19, 2003
Speaker: Dr. Michael O'Connell, Director, BioPharmaceutical Solutions, Insightful Corporation

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Abstract
Data management and analysis of clinical and pre-clinical studies involves an enormous effort on the part of biostatisticians, S-PLUS and SAS programmers. There are many FDA-required studies; and efficient trial design, rigorous statistical analysis and sound clinical inference are required at all stages. In addition, substantive regulatory pressures mandate a validated data management and analysis environment. This must be maintained in parallel to a creative statistical and graphical analysis sand-box, that is needed to understand and demonstrate clinical effects throughout the project teams. This seminar describes clinical data analysis and presentation using S-PLUS including:

  • Validated analysis. Batch analysis jobs and scripted error checking of verbose log files.
  • Tabular and Graphical Reporting. Automated analysis and reporting for pre-clinical (and clinical) studies.
  • Advanced Statistical Analysis. Regression, survival and advanced analysis of clinical data.
  • Improved Clinical Trial Design. Group sequential clinical design methods.
  • Complementing SAS. Providing seamless statistical analysis and visualization for data managed and transformed using SAS.
  • Knowledge Sharing/Deployment. Advanced visualization with S+Graphlets and Web-based deployment through StatServer for improved collaboration and information sharing between project teams and key decision-makers.
Learn about the exciting new features in S-PLUS 6.2 for clinical data analysis and reporting.
Michael O'Connell, Ph.D., is director of Biopharmaceutical Solutions at Insightful Corp. He has more than 15 years experience in clinical informatics and the health-care statistics arena, having published more than 30 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 (generalized linear) mixed models.