|Title||An Overview of Multiple Testing Procedures for Categorical Data|
|Year of Publication||2014|
|Authors||Heyse, Joseph F.|
Multiple comparison and multiple endpoint procedures are applied universally in a broad array of experimental settings. In confirmatory clinical trials of candidate drug and vaccine products the interest is in controlling the family-wise error rate (FWER) at a specified level α. Gaining popularity in many other discovery settings is an interest in maintaining the false discovery rate (FDR) as an attractive alternative to strict FWER control. Yosef Hochberg made impactful contributions to both FWER methods (Hochberg, 1988) and FDR methods (Benjamini and Hochberg, 1995) which are widely used in biopharmaceutical applications.
When one or more of the hypotheses being tested is based on categorical data, it is possible to increase the power of FWER and FDR controlling procedures. This talk will trace the development of multiple comparison procedures for categorical data, starting with a proposal by Mantel (1980), and continuing to the development of fully discrete FDR controlling procedures. Special attention will be given to Hochberg's contributions. The situation with multiple correlated endpoints will also be discussed. Simulations and theoretical arguments demonstrate the clear power advantages of multiplicity procedures that take proper accounting of the discreteness in the data.
An Overview of Multiple Testing Procedures for Categorical Data