Title
Decision Paths approach for Non-inferiority and Superiority Testing with Multiple Endpoints
Speaker
Yi Liu, The Ohio State University
Abstract
Testing for efficacy with multiple endpoints has emerged as an important statistical problem due to increasing desire to include secondary endpoints on drug labels. For example, label for the breast cancer drug Herceptin lists a primary endpoint and three secondary endpoints. In accordance with the Prescription Drug User Fee Act of the U.S. Congress (PDUFA IV), The Food and Drug Administration (FDA) will issue a guidance on Multiple Endpoints in 2009.
Such statistical problems have defined paths to decision-making. With primary and secondary endpoints, efficacy in a secondary endpoint is only relevant if efficacy in the primary endpoint has been shown. Similarly, in non-inferiority and superiority testing, superiority is only relevant if non-inferiority has been shown.
Current approach to such problems is based on closed testing, testing all possible intersection hypotheses, and collating the results. For decision-making to follow pre-defined paths, strategic choices of test statistics and critical values must be made. As the number of doses and endpoints increase, such strategic choices become increasingly difficult.
Instead of closed testing, we propose to formulate partitioning hypotheses using the Decision Path Principle. Decision making from testing these hypotheses automatically respect pre-defined paths. The number of hypotheses tested is also drastically reduced, facilitating implementation and interpretation.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.
