The POWER Procedure
- Overview
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Getting Started
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Syntax
PROC POWER Statement LOGISTIC Statement MULTREG Statement ONECORR Statement ONESAMPLEFREQ Statement ONESAMPLEMEANS Statement ONEWAYANOVA Statement PAIREDFREQ Statement PAIREDMEANS Statement PLOT Statement TWOSAMPLEFREQ Statement TWOSAMPLEMEANS Statement TWOSAMPLESURVIVAL Statement TWOSAMPLEWILCOXON Statement
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Details
Overview of Power Concepts Summary of Analyses Specifying Value Lists in Analysis Statements Sample Size Adjustment Options Error and Information Output Displayed Output ODS Table Names Computational Resources Computational Methods and Formulas ODS Graphics ODS Styles Suitable for Use with PROC POWER
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Examples
One-Way ANOVA The Sawtooth Power Function in Proportion Analyses Simple AB/BA Crossover Designs Noninferiority Test with Lognormal Data Multiple Regression and Correlation Comparing Two Survival Curves Confidence Interval Precision Customizing Plots Binary Logistic Regression with Independent Predictors Wilcoxon-Mann-Whitney Test
- References
| Error and Information Output |
The Error column in the main output table provides reasons for missing results and flags numerical results that are bounds rather than exact answers. For example, consider the sample size analysis implemented by the following statements:
proc power;
twosamplefreq test=pchi
method=normal
oddsratio= 1.0001
refproportion=.4
nulloddsratio=1
power=.9
ntotal=.;
run;
| Fixed Scenario Elements | |
|---|---|
| Distribution | Asymptotic normal |
| Method | Normal approximation |
| Null Odds Ratio | 1 |
| Reference (Group 1) Proportion | 0.4 |
| Odds Ratio | 1.0001 |
| Nominal Power | 0.9 |
| Number of Sides | 2 |
| Alpha | 0.05 |
| Group 1 Weight | 1 |
| Group 2 Weight | 1 |
| Computed N Total | ||
|---|---|---|
| Actual Power | N Total | Error |
| 0.206 | 2.15E+09 | Solution is a lower bound |
The output in Figure 70.6 reveals that the sample size to achieve a power of 0.9 could not be computed, but that the sample size 2.15E+09 achieves a power of 0.206.
The Info column provides further details about Error column entries, warnings about any boundary conditions detected, and notes about any adjustments to input. Note that the Info column is hidden by default in the main output. You can view it by using the ODS OUTPUT statement to save the output as a data set and the PRINT procedure. For example, the following SAS statements print both the Error and Info columns for a power computation in a two-sample 
test:
proc power;
twosamplemeans
meandiff= 0 7
stddev=2
ntotal=2 5
power=.;
ods output output=Power;
run;
proc print noobs data=Power; var MeanDiff NominalNTotal NTotal Power Error Info; run;
The output is shown in Figure 70.7.
| MeanDiff | NominalNTotal | NTotal | Power | Error | Info |
|---|---|---|---|---|---|
| 0 | 2 | 2 | . | Invalid input | N too small / No effect |
| 0 | 5 | 4 | 0.050 | Input N adjusted / No effect | |
| 7 | 2 | 2 | . | Invalid input | N too small |
| 7 | 5 | 4 | 0.477 | Input N adjusted |
The mean difference of 0 specified with the MEANDIFF= option leads to a "No effect" message to appear in the Info column. The sample size of 2 specified with the NTOTAL= option leads to an "Invalid input" message in the Error column and an "NTotal too small" message in the Info column. The sample size of 5 leads to an "Input N adjusted" message in the Info column because it is rounded down to 4 to produce integer group sizes of 2 per group.