Capabilities

The MULTTEST Procedure

addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests
provides the following p-value adjustments:
- Bonferroni
- Šidák
- step-down methods
- Hochberg
- Hommel
- Fisher combination
- bootstrap
- permutation
- adaptive methods
- false discovery rate
- positive FDR
handles data arising from a multivariate one-way ANOVA model, possibly stratified, with continuous and discrete response variables; it can also accept raw p-values as input data
perform a t test for the mean for continuous data with or without a homogeneity assumption, and the following statistical tests for discrete data:
- Cochran-Armitage linear trend test
- Freeman-Tukey double arcsine test
- Peto mortality-prevalence (log-rank) test
- Fisher exact test
provides exact versions of the Cochran-Armitage and Peto tests that use permutation distributions and asymptotic versions that use an optional continuity correction.
use a stratification variable to construct Mantel-Haenszel-type tests
tests can be one- or two-sided
enables you to specify linear contrasts that compare means or proportions of the treated groups
creates output data sets containing raw and adjusted p-values, test statistics and other intermediate calculations, permutation distributions, and resampling information
obtain separate analyses on observations in groups
uses ODS to create a SAS data set corresponding to any table
uses ODS Graphics to create graphs as part of its output

For further details see the SAS/STAT User's Guide: The MULTTEST Procedure
( PDF | HTML )

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