In view of the known variability of industrial hygiene data sets, a small dataset is unlikely to generate data which reflects the full range of potential exposures. Rule-based data interpretation approaches including the EN 689 standard can be applied for small or highly truncated data sets where most of the values are below the limit of quantitation. Rule-based statistics are easy to use, are “black and white”, and often allow better decision making than calculating traditional statistics or Bayesian statistics for these small or highly truncated data sets. However, as discussed in this presentation, the choice of which rule-based data interpretation approach to apply can depend on many factors, including not only the number of data points collected but also a risk assessment for your specific operation(s).
Who Should attend?
From the laboratory bench chemist to manufacturing to management; including formulation development, project team, CMC members and QA, chemists, biologists and operators, Drug innovators, CMOs, and small to large pharma and biotech.
Dr. Brent Altemose, Ph.D., CIH, CSP is a Principal Industrial Hygienist with Trinity Consultants. Since beginning his career as a ventilation engineer, he has worked for over 20 years in the fields of industrial hygiene and occupational safety. Dr. Altemose has particular expertise in exposure control, exposure assessment strategies and modeling, analysis of industrial hygiene data, local exhaust and laboratory ventilation, and indoor air quality.
Note: The course fee is per attendee. Recording the training course is strictly prohibited.
For registration questions please contact Kent Rader at Kent.Rader@safebridge.com
Sep 08, 2020 -- Online
11:00 AM Eastern time