MULTTEST Procedure
The MULTTEST procedure addresses the multiple testing problem by adjusting the pvalues from a family
of hypothesis tests.
PROC MULTTEST approaches the multiple testing problem by adjusting the pvalues from a family of hypothesis tests.
An adjusted pvalue is defined as the smallest significance level for which the given hypothesis would be rejected,
when the entire family of tests is considered. The decision rule is to reject the null hypothesis when the adjusted
pvalue is less than α. For most methods, this decision rule controls the familywise error rate at or below
the α level. However, the false discovery rate controlling procedures control the false discovery rate at or
below the α level. The following are highlights of the MULTTEST procedure's features:
 provides the following pvalue adjustments:
 Bonferroni
 Šidák
 stepdown methods
 Hochberg
 Hommel
 Fisher combination
 bootstrap
 permutation
 adaptive methods
 false discovery rate
 positive FDR
 handles data arising from a multivariate oneway ANOVA model, possibly
stratified, with continuous and discrete response variables; it can also accept raw pvalues as input data
 performs a t test for the mean for continuous data with or without a homogeneity
assumption, and the following statistical tests for discrete data:
 CochranArmitage linear trend test
 FreemanTukey double arcsine test
 Peto mortalityprevalence (logrank) test
 Fisher exact test

 provides exact versions of the CochranArmitage and Peto tests that use permutation distributions and
asymptotic versions that use an optional continuity correction.
 enables you to use a stratification variable to construct MantelHaenszeltype tests
 enables you to perform one or twosided tests
 enables you to specify linear contrasts that compare means or proportions of the treated groups
 creates output data sets containing raw and adjusted pvalues, test statistics and
other intermediate calculations, permutation distributions, and resampling information
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 creates a SAS data set that corresponds to any table
 automatically creates graphs by using ODS Graphics

For further details see the MULTTEST Procedure
Examples