Component Type: Tutorial
Preregistration required and is an additional fee.Statistical methods are a powerful tool used in clinical trials to assess whether the data support evidence of a treatment effect. It is therefore key that the results of any statistical analysis are interpreted correctly. However, understanding and interpreting statistical results can be challenging for non-statisticians. In this short course, participants will be introduced to common statistical methods applied to specific outcomes and their results. Some examples of potential statistical concepts to be covered can include commonly seen hypothesis test, survival analyses, regression modelling, MMRM, and adjusting for multiplicity. Important concepts such as p-values will be explained fully. Idealized examples as well as examples from publicly posted FDA reviews and results on clinicaltrials.gov will be used. Group discussions using example outputs (including examples from FDA reviews) will give participants the opportunity to apply their learning by critically evaluating statistical analysis approaches and interpreting statistical results, thus giving participants the tools necessary to effectively communicate with their colleagues involved in drug development. Group activities and slide-based learning will be used to introduce commonly used statistical concepts that are used in the analysis of clinical trials in a simple, easy to understand manner for anyone involved in clinical trials.
Who should attend? Professionals involved in or interested in clinical trials who wants to understand how to interpret the results of common statistical analyses and have more effective communications with their statistical team members.
Learning Objectives At the conclusion of this short course, participants should be able to:
- Determine why certain statistical analyses are applied to specific outcomes
- Analyze statistical summaries and extract the important information
- Evaluate the statistical results using easily understandable language
