Introduction to Multiple Comparison Methods
The course consistes of a mixture of presentations and practicals. It begins with an overview of the multiplicity problem, basic definitions and some important historial methods. We then move onto parametric multiple inference in the linear model, and introduce the max T method which forms the basis for procedures such as Tukey and Dunnett's multiple comparison procedures.
The remainder of the course concentrates on more recent and increasingly popular multiple comparison methods, particularly in clinical trials. The Closure Testing Principle, which forms the basis of many methods, is introduced and common stepwise procedures discussed. We then move from "data driven" methods to fixed sequence methods and their variations. Gatekeeping procedures for controlling type 1 error in the face of multiple objectives and the inclusion of primary, secondary and tertiary endpoints in regulatory claims are discussed.
The course finishes with further consideration on the analysis of multiple endpoints.
Who Should Attend?
Medical statisticians, and related, engaged in clinical trials and medical related research, who have little or no experience of dealing with multiplicity issues. Experience of SAS and R would be advantageous but is not essential.
How You Will Benefit
This course gives a practical introduction to multiple comparison methods from basic methods through to more recent sophisticated approaches.
What Do We Cover?
Practical work will be done in: SAS supplemented by R where required.
Note: For practical work, participants must bring their own laptop with fully licensed versions of the software.