Mixed Model Power
computes the statistical power of a set of onedegreeoffreedom
hypothesis tests
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
ESTIMATE statements
), and ranges of values for
alpha
and effect sizes, and the process outputs a table of power values calculated using a noncentral
t

distribution
.
Two data sets are required to run
Mixed Model Power
. The first is the
Experimental Design Data Set (EDDS)
.
This data set provides information about the design, typically for one gene or protein, of the proposed experiment. It must include all relevant design
variables
of the experiment for which you want to compute power. The sample size equals the number of rows in this data set.
The second required file contains PROC MIXED ESTIMATE statements See
Estimate Builder
for more details. ESTIMATE statements are used to specify linear hypotheses of interest that are valid for each specified
fixed effects
model. Distinct power values are computed for each hypothesis test.
The output of the
Mixed Model Power
process includes one output data set listing the
tstatistics
and
Overlay Plot
s showing the associated power values for each multiplier and each level of
alpha
(not shown) and the associated power curves. This output is accessed from the tabbed
Results
window.
Effect sizes (
log
2
differences) are plotted along the xaxes. Power is plotted along the yaxis of each plot. The greater the power, the higher the probability of rejecting the
null hypothesis
(in this case, there is no difference in expression due to the experimental variable) when the observed difference is real. Note that, as might be 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.