This process calculates
Mahalanobis distance
based on available data to identify subject inliers and outliers in multivariate space from the multivariate
mean
. It also generates results by site to see which sites are extreme in this multivariate space.
Mahalanobis distance is plotted on the log scale to allow for easier examination of small scores. The reference line is derived from a
transformation
of the mean of the approximate
chi-square
distribution
.
This process attempts to use as much data as possible. Along with sex and age, it takes all findings test codes by visit number and time number (if available), as well as frequencies of all event and intervention codes per subject. Of course, doing so can lead to missing data particularly for studies that do not appear to have a fixed number of visits or with lots of dropouts. Because Mahalanobis distance cannot be calculated with missing data present, there is an option to delete
variables
with at least
X
% of missing data based on the selected
population
and filters (default of 5%). Of remaining variables, scores are computed for those subjects with complete data. The general strategy of this process is to use as many variables as possible, while letting a few early dropouts fall out of the analysis.
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Mahalanobis Distance
: Presents plots of
Mahalanobis distance
of all subjects (distance is from the multivariate
mean
), colored by study site, and
Box Plot
s presented by sites.
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Missing Data
: Details
variables
that contain missing data that prevented Mahalanobis Distance from being calculated for certain subjects (
Flag = 1
) or variables that were dropped from analysis based on the
dialog
option
Remove variables from analysis with a missing data percentage of at least:
. By default, variables with
5
% or more of missing data are
not
used in the calculation of Mahalanobis Distance.
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Profile Subjects
: Select subjects and click
to generate the patient profiles. See
Profile Subjects
for additional information.
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Show Subjects
: Select subjects and click
to open the
ADSL
(or
DM
if
ADSL
is unavailable) of selected subjects.
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Cluster Subjects
: Select subjects and click
to cluster them using data from available domains. See
Cluster Subjects
for additional information.
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Create Subject Filter
: Select subjects and click
to create a subject filter. Follow-up analyses are subset to these subjects if the
Subject Filter
is applied in the dialog.
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Click
to view the associated data tables. Refer to
View Data
for more information.
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Output includes one summary data set (named
csass_sum_XXX
1
, by default) containing one record per subject with pre-dosing data, one data set of all pairwise distances within the
covariate
subgroups (named
csass_alldist_XXX
, by default), one data set containing minimum pairwise distances for each covariate subgroup (named
csass_mindist_XXX
), by default), one data set per covariate subgroup containing pairwise distances (named
csass_p_Y_XXX
, by default, where
Y
is indexed 1 to the number of covariate subgroups) and one data set per covariate subgroup containing the
distance matrix
of subjects within the covariate subgroup (named
csass_Y_XXX
, by default, where
Y
is indexed 1 to the number of covariate subgroups).
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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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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 process dialog used to generate this output.
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The
_XXX
designation is used to designate a one- to three-digit number that is added sequentially to prevent overwriting of existing data sets.