The
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
mixed models
, it enables you to calculate power curves for a range of Type 1 error probabilities (
alpha
). 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
expression
, for example) are deemed statistically significant. Conversely, this process also enables you to calculate the statistical
power
of an experiment, given a specified
sample size
. 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
variance
components.
Mixed Model Power
computes the statistical power of a set of one-degree-of-freedom
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. The process outputs a table of power values calculated using a noncentral t-distribution.
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The
Experimental Design Data Set (EDDS)
.
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.
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The file containing
PROC MIXED ESTIMATE
statements. 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. See
Estimate Builder
for more details.
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For detailed information about the files and data sets used or created by JMP Life Sciences software, see
Files and Data Sets
.
The output of the
Mixed Model Power
process includes one output data set listing the
t-statistics
and associated power values for each level of
alpha
(
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
null hypothesis
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.