sousatmultiple

Sousatmultiple is a statistical technique used to extend the power of hypothesis tests in situations where the number of comparisons being made is very large, rendering traditional Holm-Bonferroni adjustment and family-wise error rate (FWER) control methods impractical.

Developed by Marcello Framarino dei Toni and Barbara Pacini in 2003, the method was designed to handle multiple testing problems by focusing on power, not just error rate control. It achieves this by taking account of the variance of the test statistic in the calculation of the p-value of a hypothesis test.

The method is based on the observation that the variance of the test statistic can be used to correct the p-value in a way that preserves its tail behavior, while at the same time improving power by avoiding the conservative Bonferroni adjustment approach. This is done through a reweighting of the test statistic, based on its observed variance.

Although sousatmultiple offers an attractive solution to the multiple testing problem in certain situations, its use and interpretation require a deep understanding of statistical theory and practice. This is due to the method's sophisticated and complex mathematical underpinnings. Despite these challenges, sousatmultiple has found utility in fields such as genetics, neuroscience, and medicine, where researchers seek sensitive and specific identification of differentially expressed genes or proteins.

The technique has been the subject of considerable interest and research in the statistical community, especially in the areas of multiple testing and data analysis, where its potential for providing more powerful and flexible statistical inference has been widely recognized. However, as with any other statistical method, sound custom and judgment are crucial for the application of sousatmultiple in practice.