The
Cluster Subjects Across Study Sites
report is used to identify similar subjects. It t does so by constructing a cross domain data set using as much data as possible (subject to user options). Next, it computes a
distance matrix
and performs
hierarchical clustering
of subjects across all of the study centers. The goal of this exercise is to identify pairs of subjects with a very small distance. This could be an indication that these subjects are in fact the same individual who has enrolled at multiple sites.
Running this report using the
Nicardipine
sample setting and default options generates the output shown below. Refer to the
Cluster Subjects Across Study Sites
requirements description for more information. This report uses pre-dosing information with the goal of identifying subjects that have enrolled at two or more clinical sites.
The
Cluster Subjects Across Study Sites
report shows the results of clustering of the subjects on the basis of different combinations of covariates (demographic groups in this example). The results for each grouping are presented on a separate “tab”.
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.
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Between-Subject Distance Summary (Cluster Subjects Across Study Sites)
:
Box plots
are presented for all pairwise distances between subjects in the selected
population
. Pairs are limited based on selections from the
Cluster subjects matching these criteria
panel of the
dialog
.
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In this example, box plots are presented by gender and race. The more similar a pair of subjects, the smaller the distance value (a zero indicates a perfect match). The minimum distance from each
covariate
subgroup is presented in the box plot to the right. The subgroup with the most similar pair of subjects is presented on the next tab.
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One or more
Subgroup Clustering
sections: Only one tab is opened initially. The name of this tab is dependent on the covariate values used (as specified in the
Cluster subjects matching these criteria
panel) and the subgroup that is identified with the minimum pairwise distance.
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In this example, the Sex =F, Race WHITE subgroup shows the minimum pairwise distance. Other subgroup results can be opened from the
Results Sections sections
menu.
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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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Show Rows in Heat Map
: Select points that represent pairs of subjects in the
Box Plot
s
and click
to highlight the subjects within the
Heat Map and Dendrogram
to see how they cluster together.
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Subset Clustering
: On a subgroup
clustering
page, subsets clustering to subjects, based on pairs selected from corresponding box plot.
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Revert Clustering
: Click
to return a subset clustering to the original state where all subjects are clustered.
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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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Refer to
Add Holiday or Event
for a description of the output data and files generated by this process.
The
_XXX
designation is used to designate a one- to three-digit number that is added sequentially to prevent overwriting of existing data sets.