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SAS/STAT Topics

SAS/STAT Software

Power and Sample Size

Power and sample size analysis optimizes the resource usage and design of a study, improving chances of conclusive results with maximum efficiency. The standard statistical testing paradigm implicitly assumes that Type I errors (mistakenly concluding significance when there is no true effect) are more costly than Type II errors (missing a truly significant result). This may be appropriate for your situation, or the relative costs of the two types of error may be reversed. Power and sample size analysis can help you achieve your desired balance between Type I and Type II errors. With optimal designs and sample sizes, you can improve your chances of detecting effects that might otherwise have been ignored, save money and time, and perhaps minimize risks to subjects.

The SAS/STAT power and sample size procedures include the following:

GLMPOWER Procedure


Power and sample size analysis optimizes the resource usage and design of a study, improving chances of conclusive results with maximum efficiency. The GLMPOWER procedure performs prospective power and sample size analysis for linear models, with a variety of goals:

The following are highlights of the GLMPOWER procedure's features:

  • statistical analyses that are covered include Type III F tests and contrasts of fixed effects in univariate and multivariate linear models, optionally with covariates
  • for multivariate models, you can choose from the following tests:
    • Wilks' lambda
    • Hotelling-Lawley trace
    • Pillai's trace
  • for the univariate approach to repeated measures, you can choose from the following types of F tests:
    • uncorrected
    • Greenhouse-Geisser
    • Huynh-Feldt
    • Box conservative
  • supports BY group processing, which enables you to obtain separate analyses for grouped observations
  • creates a SAS data set that corresponds to any output table
  • automatically creates graphs by using ODS Graphics
For further details, see GLMPOWER Procedure

POWER Procedure


The POWER procedure performs prospective power and sample size analyses for a variety of goals, such as the following:

  • provides analysis for the following:
    • t tests, equivalence tests, and confidence intervals for means
    • tests, equivalence tests, and confidence intervals for binomial proportions
    • multiple regression
    • tests of correlation and partial correlation
    • one-way analysis of variance
    • rank tests for comparing two survival curves
    • logistic regression with binary response
    • Wilcoxon-Mann-Whitney (rank-sum) test
    • Cox proportional hazards regression
    • Farrington-Manning noniferiority tests of relative risk
  • determining the sample size required to get a significant result with adequate probability (power)
  • characterizing the power of a study to detect a meaningful effect
  • conducting what-if analyses to assess sensitivity of the power or required sample size to other factors
  • creates a SAS data set that corresponds to any output table
  • automatically creates graphs by using ODS Graphics
For further details, see POWER Procedure