The sequential goodness-of-fit (SGoF) multiple testing method has recently been proposed as an alternative to the familywise error rate-And the false discovery rate-controlling procedures in high-dimensional problems. For discrete data, the SGoF method may be very conservative. In this paper, we introduce an alternative SGoF-type procedure that takes into account the discreteness of the test statistics. Like the original SGoF, our new method provides weak control of the false discovery rate/familywise error rate but attains false discovery rate levels closer to the desired nominal level, and thus it is more powerful. We study the performance of this method in a simulation study and illustrate its application to a real pharmacovigilance data set.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work of the first and third authors was supported by grant MTM2011-23204 (FEDER support included) of the Spanish Ministry of Science and Innovation. The work of the second author was supported by grant 41075010 of the Center of Research and Development of Darmstadt University of Applied Sciences.