This report screens all findings measurements for a specified domain one at a time by performing a repeated-measures analysis of
variance
. The baseline measurements can be considered as a
covariate
or as a response. A measurement is determined to be a baseline measurement by the
xxBLFL
variable where
xx
is substituted with the 2-letter code for the chosen domain for analysis. If this
variable
does not exist, baseline is calculated from measurements taken on or before day 1 of the study. A time can be specified to determine baseline measurements. A compound symmetry
covariance
structure is assumed within each subject. A separate
model
is fit for each lab measurement. You can also separate the data into time s. A
volcano plot
of the interaction effect and other output enable efficient screening of lab scores that differ between treatment groups.
Note
: JMP Clinical uses a special protocol for data including non-unique Findings test names. Refer to
How does JMP Clinical handle non-unique Findings test names?
for more information.
Running this report for
Nicardipine
using default settings generates the tabbed
Report
shown below. Results organized in sections. Each sections contains one or more plots, data panels, data filters, or other elements that facilitate your analysis.
This pane enables you to access and view the output plots and associated data sets on each tab. Use the drop-down menu to view the section in the
Results
pane or remove the section and its contents from the
Results
pane.
Shows the primary results from the analysis, including
Volcano Plot
s
and various analyses on least squares means.
Note
: The name of this section and the findings results (
LB
,
VS
, or
EG
) displayed depend on the domain selected using the
Findings Domain to Analyze
option.
This section provides a comprehensive summary of
ANOVA
model fitting results. It is important to keep in mind which
model
was fit and to carefully consider hypotheses of interest. Depending on the variability in your data and your objectives, you might wish to alter the significance criterion to obtain fewer or more significant Findings tests. The numerous -down options are valuable for exploring interesting subsets.
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A series of
volcano plots
.
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Volcano plots
are a convenient way to summarize a specific
hypothesis test
across all Findings tests. Each plot is based on a single hypothesis of interest and each point in the plot is a Findings test. The
X
axis represents a difference or estimate and the
Y
axis its corresponding
-log
10
(
p-value
). Volcano plots have a characteristic "V" shape because estimates near
zero
(0) tend not to be significant and those away from zero tend to have smaller
p
-values and larger
-log
10
(
p
-values). Significant Findings tests are those in the upper left and right quadrants of the plot, akin to exploding pieces of molten lava. The red dashed horizontal line usually represents a significant criterion computed by some multiple testing method like
FDR
. You can change this value with an action button in the left panel. You can also resize all of the plots with a slider above them.
When interpreting volcano plots, it is important keep in mind the direction of the difference on the
X
axis. For example, the plot from the
Nicardipine
example shown above has
Diff of TRT01P = (NIC .15)-(Placebo)
on the
X
axis. Positive differences are located on the right side, whereas negative differences are located on the left.
You can mouse over points of interest to see their labels or select points by dragging a mouse rectangle over them. Use the
lasso tool
to select irregular regions. To find specific Findings tests whose identifier you know, click
Results
in the
Tabs
section, and then click
View Data
. In the subsequently opened data table, click
Edit > Search
, and type in the desired search string. Any Findings tests that you select in the table is highlighted in the graphs and vice versa. Selected Findings tests are highlighted in other plots and you can also then click on various
Down Buttons
on the left-hand side for further analyses on those specific Findings tests.
Volcano plots are generated for the set of
LS means
you specify in the input
dialog
(for example, all possible pairs or differences with a control) as well as for all custom
ESTIMATE statements
that you specify.
See
Volcano Plot
for more information.
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This plot enables you to compare
expression
patterns for all significant Findings tests simultaneously. The standardized least squares means for every Findings test that is significant in at least one volcano plot are clustered both horizontally and vertically and depicted with a heat map. The
standardization
is to
mean
zero
(0) and
variance
one
(1). Each row of the heat map is a Findings test and each column is a distinct LS mean. You can see which Findings tests and LS means have similar profiles. You can click on branches of the horizontal dendrogram to select all Findings tests in that cluster. These Findings tests are then highlighted in other plots, and you can click on the
Down Buttons
on the left-hand side for further analyses.
Click and slide the
cross-hair point
at the top or bottom of the horizontal dendrogram to change the number of colored cluster groups.
See
Heat Map and Dendrogram
for more information.
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A
parallel plot
of LSMeans.
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See
Parallel Plot
for more information.
This plot provides an alternative way of comparing significant LS means. It computes a
principal components
analysis on them and plots the first two components. This projects high-dimensional patterns into two dimensions. Findings tests that cluster together in this plot tend to also cluster together in the
hierarchical clustering
and parallel plots. This plot can help identify outliers. Points near the outer virtual bounding ellipse are well-explained by the first two principal components.
See
Principal Components Analysis Plot
for more information.
The
Variability Estimates
section contains the results of a
distribution
and multivariate analysis for each sample.
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These show the distributions of each of the
variance
component estimates from the fitted
ANOVA
models, including
quantiles
and summary statistics. You can see which variance components are explaining the most variability across Findings (or
adverse event
) tests.
RSquare
is an approximation to the proportion of variability explained by the
model
. The quantiles can be useful when conducting a
power
and
sample size
exercise.
See
Distribution
for more information.
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Multivariate
Analysis.
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These plots provide a multivariate analysis of the variance component estimates, including their correlations and a
scatterplot
matrix. These reveal interrelationships between the components and how they compete to explain variability.
See
Scatterplot Matrix
for more information.
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Significant Differences Data Set
: This output data set contains a complete list of the Findings tests significant by one or more criteria. This data set is indicated by the
_sig
suffix. Click
to view the data set.
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Stacked Data Set
: Contains findings measurements at subject level in a stacked format. Click
to view the data set.
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Experimental Design Data Set
: This is a
SAS data set
that provides information about the columns of a tall data set. It describes relevant experimental
variables
such as treatment conditions and
covariates
as well as a variable named
ColumnName.
Refer to
The Example Data
for more information. Click
to view the data set.
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Fit Model and Plot LS Means
: Select points or rows and click
to select
variable
(s) that uniquely define wide column names. Selected Findings tests are analyzed in the JMP Fit Model platform to view detailed fitting results and plots.
Attention
: Read the
warning
found in the link.
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Construct One-way Plots
: Click
to plot the original data in one-way format using treatment variables of your choice.
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Trend Plots
: Select Findings tests of interest and click
to run the
Findings Time Trends
report to plot the time course of measurements along the trial for selected tests.
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Shift Plot
: Select Findings tests of interest and click
to run the
Findings Shift Plots
report to show differences between baseline and on-treatment findings measurements for selected tests.
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Box Plot
: Select Findings tests of interest and click
to run the
Findings Box Plots
report to show the
distributions
of measurement values for the selected tests.
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Waterfall Plot
: Click
to launch a
dialog
from which you can generate a waterfall plot to show the
distribution
of changes in test measurements for the selected Findings domain across subjects
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Click
to view the associated data tables. Refer to
View Data
for more information.
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Click
to generate a standardized
pdf
- or
rtf
-formatted report containing the plots and charts of selected sections.
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to view the SAS output file.
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Click
to take notes, and store them in a central location. Refer to
Add Notes
for more information.
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Click
to read user-generated notes. Refer to
View Notes
for more information.
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Click the
arrow to reopen the completed report dialog used to generate this output.
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Click the gray border to the left of the
Options
tab to open a dynamic report navigator that lists all of the reports in the review. Refer to
Report Navigator
for more information.
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Additional Filter to Include Subjects
2
,
Merge supplemental domain
,
Include the following findings records:
,
Additional Filter to Include Findings Tests
,
Select the population to include in the analysis
,
By Variables
Model baseline as:
,
Time Scale
,
Baseline Time Window
,
Calculate baseline as:
,
Trial Time Windows
Include T-statistics
,
Include p-values in addition to -log
10
(p-values)
,
Plot standardized residuals
Subject-specific filters must be created using the
Create Subject Filter
report prior to your analysis.
For more information about how to specify a filter using this option, see
The SAS WHERE Expression
.