Center for Professional Innovation & Education, Inc., Los Angeles, CA
July 14 – 16, 2014
The first two days of this course will introduce and detail the basic and intermediate statistical concepts that are essential for professionals in a biological, public health or medical environment. The first day will emphasize the principles of descriptive and inferential statistical applications while the second day will focus on actual study examples, problem solving and interpretation of clinical (efficacy and adverse events) results. Throughout the course, participants are encouraged to ask questions and discuss examples relevant to their own work. The following include but are not limited to topic areas to be discussed: - Basic statistical terminology needed to effectively communicate with and understand your statistical colleagues
- The statistical essentials required to initiate a research investigation and plan a clinical trial
- Research questions in statistical terms and bias reducing techniques in planning a clinical trial
- Sample size considerations to insure accuracy of conclusions in clinical trials to determine treatment efficacy. A discussion of ethical considerations in sample size planning
- Examination of Phase I (adverse events) and dose response studies
- Discussion of statistical techniques to compare experimental approaches or treatment efficacy with a focus on superiority outcomes
- An introduction to interim and group sequential designs as well as futility analysis
The third day of course will cover more complex issues in research investigations and clinical trials. Topics will include: - Association studies including correlation and regression analysis with clinical applications to multiple intervention strategies
- Examination of Phase II and III clinical trials analysis. Comparative studies will contrast superiority, equivalence and non -inferiority approaches to design and analysis
- Survival analysis and discussion of related techniques (hazard ratio, multivariate Cox modeling)
- Gaining information from multiple studies by meta-analysis and the challenges of combining information
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