This example demonstrates various ways you can modify and enhance plots:
assigning analysis parameters to axes
fine-tuning a sample size axis
adding reference lines
linking plot features to analysis parameters
choosing key (legend) styles
modifying symbol locations
The example plots are all based on a sample size analysis for a two-sample t test of group mean difference. You start by computing the sample size required to achieve a power of 0.9 by using a two-sided test with , assuming the first mean is 12, the second mean is either 15 or 18, and the standard deviation is either 7 or 9.
Use the TWOSAMPLEMEANS statement with the TEST=DIFF option to compute the required sample sizes. Indicate total sample size as the result parameter by supplying a missing value (.) with the NTOTAL= option. Use the GROUPMEANS=, STDDEV=, and POWER= options to specify values of the other parameters. The following statements perform the sample size computations:
proc power; twosamplemeans test=diff groupmeans = 12 | 15 18 stddev = 7 9 power = 0.9 ntotal = .; run;
Default values for the NULLDIFF=, SIDES=, GROUPWEIGHTS=, and DIST= options specify a null mean difference of 0, two-sided test, balanced design, and assumption of normally distributed data, respectively.
Output 75.8.1 shows that the required sample size ranges from 60 to 382, depending on the unknown standard deviation and second mean.
Output 75.8.1: Computed Sample Sizes
Fixed Scenario Elements | |
---|---|
Distribution | Normal |
Method | Exact |
Group 1 Mean | 12 |
Nominal Power | 0.9 |
Number of Sides | 2 |
Null Difference | 0 |
Alpha | 0.05 |
Group 1 Weight | 1 |
Group 2 Weight | 1 |
Computed N Total | ||||
---|---|---|---|---|
Index | Mean2 | Std Dev | Actual Power | N Total |
1 | 15 | 7 | 0.902 | 232 |
2 | 15 | 9 | 0.901 | 382 |
3 | 18 | 7 | 0.904 | 60 |
4 | 18 | 9 | 0.904 | 98 |