Comparisons of methods for multiple hypothesis testing in neuropsychological research.

ABSTRACT: Hypothesis testing with multiple outcomes requires adjustments to control Type I error inflation, which reduces power to detect significant differences. Maintaining the prechosen Type I error level is challenging when outcomes are correlated. This problem concerns many research areas, including neuropsychological research in which multiple, interrelated assessment measures are common. Standard p value adjustment methods include Bonferroni-, Sidak-, and resampling-class methods. In this report, the authors aimed to develop a multiple hypothesis testing strategy to maximize power while controlling Type I error. The authors conducted a sensitivity analysis, using a neuropsychological dataset, to offer a relative comparison of the methods and a simulation study to compare the robustness of the

SUBMITTER: Blakesley RE 

PROVIDER: S-EPMC3045855 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature