Output Overview Descriptions | Clinical Reports | Multivariate Inliers and Outliers

Multivariate Inliers and Outliers
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.
Running this process for Nicardipine using default settings generates the Report shown below.
The DM Distribution report initially shows Mahalanobis Distance and Missing Data ,.
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.
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.
Drill Down Buttons
Drill down buttons, provide you with an easy way to drill down into your data. The following drill down buttons are generated by this process:
Profile Subjects : Select subjects and click to generate the patient profiles. See Profile Subjects for additional information.
Show Subjects : Select subjects and click to open the ADSL (or DM if ADSL is unavailable) of selected subjects.
Cluster Subjects : Select subjects and click to cluster them using data from available domains. See Cluster Subjects for additional information.
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.
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).
Click to generate a standardized pdf - or rtf -formatted report containing the plots and charts of selected sections.
Click the Options arrow to reopen the completed process dialog used to generate this output.

The _XXX designation is used to designate a one- to three-digit number that is added sequentially to prevent overwriting of existing data sets.