Processes | Utilities | Mixed Model Power

Mixed Model Power process assists you in the planning of your experiments. Starting with an exemplary experimental design data set and parameter settings for a relevant , it enables you to calculate power curves for a range of Type 1 error probabilities ( ). In other words, this process helps you decide how big an experiment you need to run in order to be reasonably assured that the true effects in the study (change in gene , for example) are deemed statistically significant. Conversely, this process also enables you to calculate the statistical of an experiment, given a specified . This process is typically run before you perform your experiment, and it helps to have conducted a pilot study in order to determine reasonable values for components.
computes the statistical power of a set of one-degree-of-freedom arising from a mixed linear model. You specify an experimental design file, parameters for relevant PROC MIXED statements (including fixed values for the variance components and ), and ranges of values for alpha and effect sizes. The process outputs a table of power values calculated using a noncentral t-distribution.
required for Mixed Model Power :
 • . It must include all relevant design of the experiment for which you want to compute power. The sample size equals the number of rows in this data set.
 • The file containing statements. ESTIMATE statements are used to specify linear hypotheses of interest that are valid for each specified model. Distinct power values are computed for each hypothesis test. See for more details.
The output of the Mixed Model Power process includes one output data set listing the and associated power values for each level of ( not shown) and the power curves shown below .
Effect sizes ( log 2 differences) are plotted along the x-axes. Power is plotted along the y-axis of each plot. The greater the power, the higher the probability of rejecting the when the observed difference is real. Note that, as expected, power increases for all effects as the effect size increases. In other words, the greater the difference due to the effect, the more likely you are to successfully conclude that the observed difference is real.
To compute power for a new design, use to generate the design of interest, save the table as a SAS data set, and rerun Mixed Model Power using the new design as the EDDS.