This 2-day course will cover basic computational processes necessary for analysis of the data from immunoassays and bioassays. Important considerations will be examined such as determining optimal assay conditions, choosing the appropriate nonlinear or linear regression fit, calculating correct residual weights, computing practical limits of sensitivity and acceptable error, measuring nonparallelism and potency, and compensating for matrix effects. These and other computational decisions will be discussed and examples will be presented showing different considerations.

The course will also outline a comprehensive and stepwise method for inspecting immunoassays and bioassays for problems and troubleshooting failed assays. This method is based upon a statistical comparison to a set of validated, well characterised historical assays performed and validated by the laboratory. This process allows the analyst to set realistic acceptance criteria that will only flag assays with failed components, identify the failed reagent or incubation condition, gauge the impact of that failed component on the results, and indicate a course of action. Attendees will then troubleshoot the failed component from examples selected from thousands of problem assays.