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MULTTEST Procedure


The MULTTEST procedure addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests. PROC MULTTEST approaches the multiple testing problem by adjusting the p-values from a family of hypothesis tests. An adjusted p-value 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 p-value 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 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
  • performs 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.
  • enables you to use a stratification variable to construct Mantel-Haenszel-type tests
  • enables you to perform one- or two-sided tests
  • 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
  • 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 SAS/STAT User's Guide: The MULTTEST Procedure
( PDF | HTML )

Examples