A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests.
BACKGROUND
The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test.


RESULTS
It is shown that SGoF behaves especially well with small sample sizes when 1) the alternative hypothesis is weakly to moderately deviated from the null model, 2) there are widespread effects through the family of tests, and 3) the number of tests is large.


CONCLUSIONS
Therefore, SGoF should become an important tool for multitest adjustment when working with high-dimensional biological data.
Carvajal-Rodríguez
Antonio
A
Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, Vigo, Spain. acraaj@uvigo.es
de Uña-Alvarez
Jacobo
J
Rolán-Alvarez
Emilio
E
D003198:Computer Simulation; D015233:Models, Statistical; D018401:Sample Size