The GLMPOWER procedure covers power analysis for Type III F tests and contrasts of fixed effects in univariate and multivariate linear models. For univariate models, you can specify covariates, which can be continuous or categorical. For multivariate models, you can choose among Wilks’ likelihood ratio, Hotelling-Lawley trace, and Pillai’s trace F tests for multivariate analysis of variance (MANOVA) and among uncorrected, Greenhouse-Geisser, Huynh-Feldt, and Box conservative F tests for the univariate approach to repeated measures. Tests and contrasts that involve random effects are not supported.
The POWER procedure covers power 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
Cox proportional hazards regression
logistic regression with binary response
Wilcoxon Mann-Whitney rank-sum test
extensions of existing analyses that involve the chi-square, F, t, or normal distribution, or the distribution of the correlation coefficient under multivariate normality
The extensions of existing analyses consist of scalar multipliers and custom values for primary noncentrality and degrees of freedom. Important use cases include the following:
Wald and likelihood ratio tests in generalized linear models that have nominal, count, or ordinal responses
sample size inflation that is caused by correlated predictors
sample size deflation that is caused by correlation between the covariates and the response
Examples of generalized linear models include Poisson regression, logistic regression, and zero-inflated models.
The power and sample size tasks in SAS Studio cover a large subset of the analyses in the GLMPOWER and POWER procedures.