Paper 179-2010:

Paper 179-2010:
SAS Global Forum 2010
Paper 179-2010
Managing Clinical Data Standards: An Introduction to SAS® Clinical Data
Michael Kilhullen, SAS Institute Inc., Cary, NC
As the adoption of industry data standards grows, organizations must consider how to effectively implement and
manage data standards across a large—often global—user base. SAS® Clinical Data Integration is built upon a
centralized metadata repository that is ideal for centrally deploying and managing data standards. Built on SAS® data
integration technology, it provides a visual and metadata-driven environment that facilitates the conversion of data to
standard formats while collecting more detailed information about the decisions you make during this process. SAS
Clinical Data Integration provides specialized interfaces that further leverage the metadata to help you more
effectively work with and manage clinical standards. This paper will present SAS Clinical Data Integration features,
including how to import, customize, and manage standards metadata, how to monitor and analyze how users
consume metadata during the clinical development process, and how metadata is leveraged to provide consistency
and reusability across your organization.
SAS Clinical Data Integration 2.1 is a new product offering from SAS that focuses on pharmaceutical industry needs
for transforming, managing, and verifying the creation of industry mandated data standards such as the Clinical Data
Interchange Standards Consortium (CDISC). The product relies on SAS® Data Integration to provide centralized
metadata management using the SAS® Metadata Server and the tools to visually transform data. SAS Clinical Data
Integration enhances usability by adding new metadata types, plug-ins, and wizards that assist with clinically oriented
tasks such as importing data standards, creating studies and submissions, and adding specialized transformations for
transforming clinical data to a standard data model. It also leverages the SAS® Clinical Standards Toolkit to provide
validation and conformance checking.
SAS Clinical Data Integration facilitates the collection and management of metadata across your organization (Figure
1). Most metadata is generated by clinical programmers while they define how the operational data is converted to a
data standard. This is a very repeatable process but often needs to be adjusted based on variations in the way that
Figure 1: Typical Metadata Collected during a Clinical Trial
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data is collected and analyzed between studies. As multiple studies are processed, the study administrators must
ensure that the clinical programmers are creating domain tables that comply with a data standard. As new custom
domains are defined, or existing domains are modified, the data standards administrator must be able to understand,
evaluate, and potentially apply the changes to the centralized data standards. Finally, a study administrator is likely
overseeing several studies that might have different clinical programmers assigned. It is important to maintain a
consistent work environment, especially if clinical programmers need to work on studies simultaneously, or transition
to a new study more quickly. Moreover, because metadata is being collected at all stages of the process, managers
can use metadata to monitor the transformation process and generate reports.
In SAS Clinical Data Integration, clinical metadata is stored in
the metadata server as properties of metadata objects. Most
metadata is related to creating standardized data and is stored
as part of the table and column metadata objects. In some
cases, new metadata objects are created to specifically address
the business needs of managing standards. For example,
studies and submissions have their own metadata
representation in the SAS Metadata Server that is based on a
common metadata object called a clinical component (Figure
2). In addition to having specialized metadata about the study
Figure 2: Clinical Metadata Types
or submission, clinical metadata objects they also catalog the
metadata contents (such as jobs, reports, tables, and so on) created by users, define the versions of standards that
are allowed for a study or submission, and enable additional processes such as importing, exporting, and archiving.
SAS Clinical Data Integration ensures that data standard domains are implemented only as part of a data standard,
study, or submission. When data standard domains are implemented in a data standard, we refer to these as
templates because they are used as the basis for the actual data and metadata collected by a study or submission.
SAS Clinical Data Integration can be configured by an administrator to create default metadata content when clinical
components are created (Figure 3). This allows you to maintain consistent content between studies or submissions.
For example, the administrator can define a standard metadata folder structure for studies. When the new study
wizard finishes, it creates the metadata folders (Figure 4). The administrator can also define standard libraries to
Figure 3: Default Content Defined for Studies
Figure 4: Content Created for a New Study
Based on Defaults
ensure that the correct SAS library statements are generated. This scenario is useful when your organization
implements standard reporting macros that depend on specific library references to gain access to data and store
results. Notice in Figure 4 that the library reference names are updated to include the study name to promote better
usability when you need to select a library from a list of all library objects.
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In addition to default content, SAS Clinical Data Integration also helps manage the clinical content within a folder. As
a best practice, a folder should contain tables with the same type of data, and typically should use the same SAS
library for physical storage. This means that you should not find CDISC Study Data Tabulation Model (SDTM) tables
mixed with CDISC Analysis Data Model (ADaM) tables in the same folder. (Figure 4 reflects this best practice). SAS
Clinical Data Integration plug-ins enforce this rule by defaulting selection windows based on existing content within
the selected folder. For example, once a domain is created using the SDTM 3.1.1 data standard, only new domains
based on the same standard version can be added to the folder.
Data standards are managed exclusively in the SAS Metadata Server. Therefore, every user of SAS Clinical Data
Integration has access to the same domain templates, validation checks, and terminology. All data standard related
plug-ins are designed to use the centralized metadata and help simplify creating domains during study
transformations (Figure 5). Clinical programmers do not need to figure out where the standards are stored or which
standards to use for a particular study. Instead, the study manager defines these settings as part of the study
Clinical DI Plug-ins
Centralized Metadata Store
Column Groups
Figure 5: Example of Leveraging Centralized Data Standards with Plug-ins
definition, and the plug-ins ensure that only applicable standards are displayed to programmers. For example, if a
Study Manager defines the study to use SDTM version 3.1.2, the SAS Clinical Data Integration plug-ins will not
display any other SDTM versions.
Accessing the domain or domain template metadata is the
same for administrators and programmers. Clinical metadata is
accessible through standard SAS® Data Integration Studio
dialog boxes that are enhanced by SAS Clinical Data
Integration plug-ins. Additional tabs are added where the
clinical metadata is displayed (Figure 6). If you have edit
permissions, then you can change the values of the clinical
metadata. Changes are considered study or submission
specific. That is, changes do not propagate back to the data
standard template. The same is true of changes to data
standard templates; they do not propagate to registered
domains. Other tools are available in SAS Clinical Data
Integration to compare domains to templates and selectively
apply changes if needed.
Clinical properties are displayed on property tabs as a table
(Figure 7). Depending on how properties are defined, they
Figure 6: Table and Column Clinical Properties
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might be displayed as a text or a selection list. Properties will have default settings, but you can adjust them
depending on how you use the standards in your organization.
Figure 7: Clinical Properties Tab
SAS Clinical Data Integration supports a common property model for data standards, studies, submissions, and
domains. The property model is largely based on the SDTM. However, it has been generalized so that you can add
your own modified SDTM implementations or internal data standards. The properties collected for clinical objects are
summarized in Table 1. In addition, you can add custom properties and notes to any metadata object.
Clinical Metadata Type
Additional Properties Managed by SAS Clinical Data Integration
Data Standard
Formal name, version, type, base standard type, vendor, supports
toolkit validation
Study title, short study title, phase
Submission title, short submission title
Identifier, purpose, classification, structure, archive location, archive title
Domain column
Term, origin, role, core, display format, qualifiers, XML type, XML
codelist, algorithm, whether the column contributes to the key
Table 1: Additional Properties Available for Clinical Objects
While the CDISC data standards dictate what you need to collect, and in some cases what a property should contain,
how your company uses a data standard will vary depending on how well the data standard integrates into your
established business processes. In some cases, the values are strictly implemented. That is, clinical programmers
can select only from a list of allowable values. If a value is not found, you need to contact the data standards
administrator to add the content. When the data standards administrator adds the value, you can then edit the
properties and select the value. Other companies prefer a less strict approach. If a value is not available in a list,
you can type in a new value. The value you provide is considered study specific and does not propagate to the data
standard. Rather, the data standards administrator periodically evaluates the values used for a property across many
studies and adjusts the standard values accordingly. Other companies prefer to simply have the user type in values
without a list. Whichever approach fits your business needs, SAS Clinical Data Integration provides the flexibility to
allow you to define how a property is collected, including whether the property is required, dependent on a lookup list,
customizable by the user, and so on (Figure 8).
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Figure 8: Column Property Model Editor
Certain CDISC standards, such as SDTM, support user-defined domains, which are domains needed for clinical data
that is not defined as a standard domain in the SDTM implementation guide. In this case, the underlying data model
is used to assemble the new domain ( Figure 9). Data models are generalized into column groups that can support
both industry standards and vendor-specific data standards. Column groups are simply a logical grouping of columns
that are combined and organized into a domain. The columns within a column group will also contain default
metadata values, which will be copied to the new domain when created.
Group A
Column Set 1
Column Set 2
Column Set 3
Group B
Group C
Figure 9: Assembling Column Groups into a Domain
Column groups can also be considered conditional. That is, columns can be selected from one column set within the
conditional column group but not another column group. This would be the case for SDTM where a domain can
contain only intervention, event, or finding columns, in addition to identifier and timing columns.
If a data standard uses an underlying model, SAS Clinical Data Integration provides a wizard to guide you in defining
the custom domain. Data standard administrators can use the same wizard to design new domain templates.
Alternatively, they can identify and promote custom domains defined by users in studies and submissions to the data
standard. Finally, you might know of a custom domain created for another study that has not been promoted to a
data standard. Rather than recreate it, SAS Clinical Data Integration can copy it as long as it is based on the same
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data standard version used by your study. In doing this, metadata settings are preserved and the necessary
metadata relationships to the new study are automatically adjusted.
When domains are created in a study or submission, the metadata stored in the data standard templates are copied.
This allows users to modify the metadata according to the study requirements without impacting the metadata that
others might be using for other studies. This, of course, means that clinical programmers can change any attribute of
the domain, including labels,
lengths, formats, types, and
column order. Furthermore,
after the domain is copied, the
data standards administrator
might change the template
metadata based on trends in
how it was used in other studies.
For example, it is common to
adjust character column lengths
to avoid truncation in a study
that collected more text. If the
standard administrators find that
programmers are frequently
changing the length, they can
change the length in the domain
template so that manual
Figure 10: Refresh Domain Metadata
adjustments are no longer
necessary. SAS Clinical Data Integration provides a refresh domain plug-in that compares a domain with the data
standard template used to create it (Figure 10). It will identify differences found and allow the clinical programmer to
choose which changes to apply to the domain.
As data standards are consumed by your organization, a data standards administrator can monitor how the data
standards are being used. SAS Clinical Data Integration makes this easy because of the wealth of metadata
collected about standards and how they are used. The data standards administrator can select from a list of clinical
components that are based on the data standard
selected. The metadata about the domains created
are compared and displayed in a table that shows
you what columns were used in the domains across
the clinical components. This will be useful when
clinical programmers are consistently adding new
columns and you need to make a decision about
adding one to the domain template. Finally, custom
domains are also displayed in the comparison so
that you can see which custom domains are being
added frequently to clinical components. In this
case, the data standards administrator can choose
to promote the custom domain. This will add the
metadata for the selected custom domain to the
data standard, making it easier for other
programmers to create it.
The SAS Clinical Standards Toolkit is a SAS macro
approach to supporting clinical data standards in
Base SAS®. It supports defining data standard
domains, conversion of domains between CDISC
models, and validation and conformance checks. It
provides periodic updates when new standards and
new versions of standards are released. Once the
updates are applied to the toolkit, they are
Figure 11: Analyzing How Standards Are Being Used
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automatically detected by SAS Clinical Data Integration during the import and validation processes.
SAS Clinical Data Integration greatly simplifies the use of the SAS Clinical Standards Toolkit. First, it assumes control
of all clinical metadata. This means that once metadata is imported from the SAS Clinical Standards Toolkit, the
powerful features found in SAS Clinical Data Integration are used to manage the data standard definitions. When the
SAS Clinical Standards Toolkit macros are used in the transformation process, the metadata needed by the SAS
Clinical Standards Toolkit is automatically exported and restructured for execution. Secondly, SAS Clinical Data
Integration adds graphical user interfaces to SAS Clinical Standards Toolkit macros. This adds a metadata-driven
approach to defining SAS Clinical Standards Toolkit tasks. Finally, validation checks and terminology are also
imported into the metadata server and are displayed as manageable objects in the clinical administration interfaces.
SAS Clinical Data Integration provides a Data Standards Metadata Import Wizard to help data standards
administrators select and load metadata. This is a one-time process per model version; once metadata loaded, the
SAS Metadata Server manages changes and additions to the data standards. The wizard prompts you to select the
standard and version from the toolkit, displays the metadata content in detail for verification, and then imports the
metadata (Figure 12). Once imported, the standard and domain templates are surfaced through SAS Clinical Data
Integration Server plug-ins. Custom versions of standards can also be imported from the SAS Clinical Standards
Toolkit. Assuming that the custom data standard is registered to SAS Clinical Standards Toolkit, when the metadata
import wizard is run, the custom data standard will automatically appear in the data standard selection lists.
Figure 12: Sample Displays from the Metadata Importer
As data standard domains are implemented in studies and submissions, the structure and content can vary from the
data standard. You must periodically verify that the domains maintain conformance to the data standard. SAS
Clinical Data Integration provides a transformation to run validation checks and generate reports through the SAS
Clinical Standards Toolkit. The SAS Clinical Standards Toolkit provides 143 unique SDTM 3.1.1 validation checks.
These checks are derived from three sources: the CDISC-SDTM WebSDM™ documented checks, checks supporting
loads into the Janus study data repository, and checks added by SAS which are based on data management and
cleansing experiences building CDISC-SDTM domains using SAS products and solutions. The validation checks are
designed to enable an assessment of the consistency of data values within a specific column, between columns,
across records within a specific data set, and across data sets. In addition to the provided validation checks, the
data standards administrators can create their own customized compliance checks using the Manage Compliance
Checks dialog box (Figure 13).
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Figure 13: Customizing Compliance Checks
The CDISC-SDTM Compliance transformation is provided for use in building validation jobs. You specify the data
standard you wish to validate against, and select the domains to be assessed and the set of compliance checks you
wish to run (Figure 14). The model compliance transformation allows you to add as many checks as desired. It also
offers filtering capabilities to help find the necessary checks.
Figure 14: Examples of the CDISC-SDTM Compliance Transformation
After the job runs, two data sets are produced. The results data set contains the findings of the compliance
assessment and the metrics data set contains summary statistics on the validation process (Figure 15).
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Figure 15: CDISC–SDTM Compliance Transformation and Results Data Set
The metadata managed by SAS Clinical Data Integration can be published to CRT-DDS using the CRT-DDS
Transformation (Figure 16). This transformation extracts metadata and passes it on to the SAS Clinical Data
Standards Toolkit for define.xml creation. The transformation allows you to specify properties to control encoding, log
level processing, and an output style sheet (Figure 17).
Figure 16: CRT-DDS Define.xml Process Flow
Figure 17: CDISC-SDTM to CRT-DDS
Transformation Properties
After the job runs, a define.xml file and a results data set are produced. The Results data set documents the results
of the generation of the CRT-DDS file. The define.xml file contains summary information about each of the domain
data sets, detailed information about each column in each domain, and code list information (Figure 18).
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Figure 18: Examples of CRT-DDS
This paper has shown several key features of SAS Clinical Data Integration 2.1 related to implementing and
managing CDISC standards. By centrally collecting and managing metadata, it can be used to automate setup and
transformation processes, reuse metadata objects to expedite data standardization, feed validation and conformance
checking, and improve the administration, consistency, and use of standards within an organization.
Hunley, Eric, Gary Mehler, and Nancy Rausch. 2009. “What’s New in SAS® Data Integration Studio 4.2”
Proceedings of the SAS Global Forum 2009 Conference. Cary, NC: SAS Institute Inc.
Villiers, Peter. 2009. “Supporting CDISC Standards in Base SAS Using the SAS Clinical Standards Toolkit.”
Proceedings of the SAS Global Forum 2009 Conference. Cary, NC: SAS Institute Inc.
Your comments and questions are valued and encouraged. Contact the author at:
Michael Kilhullen
SAS Institute Inc.
[email protected]
SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS
Institute Inc. in the USA and other countries. ® indicates USA registration.
Other brand and product names are trademarks of their respective companies.
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