[English] CDISC SI Summary

Clin Eval 40 Suppl XXXI 2012
CDISC as the global standards
CDISC standards and innovations ＊
Andrea Vadakin Rebecca D. Kush
CDISC（Clinical Data Interchange Standards Consortium）
Abstract
The Clinical Data Interchange Standards Consortium (CDISC) is an open, multidisciplinary neutral
non-profit standards developing organization (SDO) that has been working through productive, consensusbased collaborative teams, since its formation in 1997, to develop global standards and innovations to
streamline medical research and ensure a link with healthcare. The CDISC mission is to develop and
support global, platform-independent data standards that enable information system interoperability to
improve medical research and related areas of healthcare. The CDISC vision is informing patient care and
safety through higher quality medical research. The CDISC suite of standards supports medical research of
any type from protocol through analysis and reporting of results. They have been shown to decrease
resources needed by 60% overall and 70-90% in the start-up stages when they are implemented at the
beginning of the research process. They are harmonized through a model that is now not only a CDISC
standard but also an HL7 (Health Level 7) standard on the path to becoming an ISO (International
Organization for Standardization) /CEN (European Committee for Standardization) standard, thus giving
the CDISC standards (harmonized together through BRIDG (Biomedical Research Integrated Domain
Group)) an international status and accreditation. This publication provides a summary of each of the
primary CDISC standards in addition to two CDISC innovations that are designed to improve the value of
the standards in terms of enabling higher quality medical research done faster and with fewer resources.
Key words
medical research, data standard, innovation, data interchange, interoperability
＊ Japanese translation of this article originally written in English is included in
. 40
Suppl XXXI：217-28．English version is published only in the journal web-site (http://homepage3.nifty.com/cont/
40sup31/p217-28eng.pdf).
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clinical research standards development organiza-
1．The CDISC Standards
tion. A multidisciplinary collaborative group of 17
organizations across the industry to was formed to
The CDISC standards are summarized, begin-
oversee the development of CDASH. The primary
ning with the data collection standard (CDASH),
aim was to identify the minimum set of data collec-
then the transport standard (ODM), and followed
tion fields needed from a clinical, scientific and
by LAB for laboratory data, SDTM, SEND, and
regulatory data collection perspective to enable
ADaM for tabulated CRF data and analysis data-
efficient data collection at the investigative sites.
sets. The Protocol Representation standard sup-
It is acknowledged that additional data fields will
ports the study plan and the submission of elements
be needed to address study-specific requirements,
of the plan to enable evaluation of the actual data
and this approach allows sponsors the flexibility to
in accordance with the plan. The BRIDG model for
determine which fields should be added to CDASH
harmonizing
recommended fields.
the
CDISC
standards
and
the
Controlled Terminology and Glossary, which sup-
CDASH is based upon the Study Data Tabulation
port the semantic interoperability across the suite
Model (SDTM), as SDTM has been established as
are then summarized. Two innovations ̶ Healthcare
the standard for submitting tabulated data content
Link and SHARE are then described at the end of
from CRFs to the FDA. While these standards are
this publication. The end-to-end support for medi-
closely related, they have differing purposes, spe-
cal research of any type should become apparent as
cifically submission (SDTM) versus data collection
these standards are envisioned to work together,
(CDASH). Basic data collection fields identified by
building quality in from the start of a study.
CDASH project teams are mapped into SDTM and
are compliant with the SDTM Implementation
1.1 Clinical Data Acquisition Standards
Guide (SDTM IG).
Harmonization (CDASH)
The scope of CDASH does not include a stan-
CDASH supports clinical and medical research
dard physical design for CRFs, as CDASH was
data collection to improve data interoperability and
originally designed for the development of stan-
quality. This is accomplished by focusing on the
dards for the content of 16 CRF safety data
development of consensus-based
content stan-
domains. Two additional domains were later added.
dards for Case Report Forms (CRF). CDASH has
A sub-project team, CDASH-ODM, has been initi-
been developed in response to issues dealing with
ated to develop machine-readable electronic CRFs
the overwhelming diversity of data formats and
(Fig. 1).
conventions across CRFs and computer operating
The CDASH standard is designed to improve
systems. This diversity led to a great deal of inef-
workflow and quality from the very start of a study.
ficiencies and opportunities for error, an issue rec-
One of its greatest benefits is to physician investi-
ognized by the U.S. Food and Drug Administration
gators at a site who gain by seeing a standardized
(FDA), among others.
CRF fields every time, whether on paper or elec-
In 2006, CDISC was approached by the
tronic, thereby reducing the number of multiple
Association of Clinical Research Organizations
data entry requirements of the past. Having a stan-
(ACRO) and FDA to lead in the development of
dard will improve the quality of data collected and
standardized case report form content, as the global
improve communication and efficiency throughout a
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Clin Eval 40 Suppl XXXI 2012
hence CDISC has developed ODM to represent
Fig. 1 Sample of CDASH demographics CRF
in ODM transport format
clinical research data and metadata in XML. XML
uses entities called tags to identify the actual data
content. Another XML feature, attributes, allows
CDISC CDASH
Annotated CRFs
for extra information to be provided if necessary.
For instance, in order to be able to interchange
data with another system or company, there is a
need for each party to know how to build the docu-
ODM Sample:
Demographics
ment and how to interpret it. This is the where
XML comes in. The ODM schema allows a machine
to check that a received XML document meets the
Conformant to
CDASH rules
ODM standard.
ODM brings several advantages to the clinical
study process. It has a single format, providing all
components needed to describe clinical research
data with attribution requirements mandated by
regulatory agencies. It reduces the number of
study, through the collection of quality data from
unique file formats a clinical application needs to
the study s initiation. In addition, this benefits all
support. ODM also has a number of unique features
involved in a biopharmaceutical development pro-
that can improve the integrity and reliability of
gram, since data are collected in essentially the
clinical data interchange. It has a flexible design
same format in which it will be submitted. Hence,
that can be adapted to any clinical study application
FDA reviewers will receive electronic data in a
where data may be needed.
standard format (SDTM), creating a more stream-
In addition to these advantages is the ability of
lined process from the outset to the completion of
ODM to assist in managing and archiving an audit
a study.
trail. ODM keeps all clinical and associated administrative data, old and new, erasing nothing. This is
1.2 The Operational Data Model (ODM)
vital to the regulatory process as specific data
ODM is designed to facilitate the interchange
points throughout a study may be subject to FDA
review (Fig. 2).
and archive of metadata and data for clinical
research. The success of ODM is most apparent
In addition to having features that work with any
when data are being collected from multiple sources.
current clinical data management system (CDM),
ODM is compliant with the FDA s 21 CRF Part
CDISC has initiated the ODM Certification
11, meaning that it meets the requirements needed
Program to ensure interoperability among avail-
to replace traditional paper records with electronic
able ODM toolsets to promote user confidence.
Define.xml is another instance where XML has
records.
ODM uses extensible markup language (XML).
been used to assist reviewers in preventing the
XML has wide acceptance as a data interchange
need to match up dataset documentation and is able
framework in other industries as well as for numer-
to handle repeat values, links and code lists, facili-
ous vendors of clinical research software products,
tating the exchange of dataset information. The
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Fig. 2 A portion of the ODM structure showing the audit trail support
to the model to handle even more complex tests.
define.xml specification defines the metadata structures that are to be used to describe the submission
LAB is a content standard that can be trans-
datasets and variables in a manner that meets or
ported in a number of different ways through dif-
exceeds the minimum FDA requirements outlined
ferent transport standards already in use for the
in the FDA s Data Definition Document.
exchange of LAB data. ASCII, HL7 and SAS
Transport can all be used, as well as ODM. An
1.3 The Clinical Laboratory Data Model
HL7 V3 message was also developed, based upon
(LAB)
the CDISC LAB content standard; however, it was
One of the largest components of clinical research
deprecated by HL7 RCRIM (Regulated Clinical
data is that which is collected from laboratories.
Research Information Management) in 2011 due to
Through the encouragement of its members, CDISC
lack of use.
brought together a group of volunteers to develop
The LAB model approach consists of two layers:
a standard model for the acquisition and inter-
the implementation layer and the content layer. The
change of laboratory data ̶ LAB. Prior existing
content layer is fixed, in that it will have standard,
standards for laboratory data were not widely
set fields. The implementation layer can be changed
accepted by the industry in that they are typically
dependent on which interchange/transport method
considered difficult to use, inefficient, have inade-
is being used (Fig. 3).
quate field definitions, non-matching population
rules with clinical research data, and are unneces-
1.4 Study Data Tabulation Model (SDTM)
sarily complex.
SDTM provides a general framework for
The CDISC LAB model was therefore developed
describing the organization of information collected
to allow interchange of test results and reference
during human and animal studies. Other CDISC
ranges and to be flexible enough to keep pace with
standards have an impact on SDTM and it is impor-
industry changes. CDISC LAB is able to support
tant to consider the way data flows through to
both cumulative data and incremental data to meet
SDTM: from CDASH and LAB into SDTM. CDISC
stakeholder needs, and extensions have been added
Controlled Terminology is also vital to SDTM, as
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Clin Eval 40 Suppl XXXI 2012
Fig. 3 CDISC LAB model layers
Implementation Layer
Content Layer
Microbiology Extension
Core Lab Data Model
Genomics Extension
consistency in terminology creates seamless data
medications. There are two categories of domains:
flow across the standards.
CDISC standard domains and custom domains, the
SDTM is built around observations gathered
latter of which can be created for areas not yet
about subjects participating in a clinical study (i.e.
modeled and allow more flexibility to cover all the
through CRFs/CDASH). Each observation is
areas needed for submission. Most observations
described by a series of variables, corresponding to
collected during a human clinical study fall into one
a row in a dataset or table. The metadata for each
of three general classes: interventions, events, or
variable includes a variable name, label, formats or
findings. Precise definitions of these three terms,
decodes and origin. These variables are classified
as well as additional Special Purpose domains can
into four major roles: identifier variables, topic
be found in the SDTM Implementation Guide (Fig.
variables, timing variables, and qualifier variables.
4).
In addition to supporting FDA reviews, SDTM is
A fifth type of variable role, rule, is currently
also very useful for supporting data aggregation
only used with the Trial Design Model.
Observations in SDTM are normally organized
across studies (e.g. in data warehouses) and for
into a series of domains consisting of groups of
exchanging/sharing study data across partners, for
related data, such as adverse events or concomitant
example, in cases of collaborative studies, acquisi-
Fig. 4 SDTM v 3.1.2 domain types and domains
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tions and mergers or when contracting out studies.
are one of the four types of data that can be sub-
Data can be mapped to SDTM at the end of a study
mitted to the FDA, along with study tabulation
or when the need for aggregation occurs; however,
datasets, subject profiles, and listings. SDTM
this is inefficient and there is a loss of quality and
datasets are intended to represent the collected
integrity in the data. It is much more efficient to
clinical data, and are not sufficient to support sta-
collect the data in a CDASH format with Controlled
tistical analysis and testing of the scientific hypoth-
Terminology in the beginning, thus enabling effi-
eses that are the purpose of conducting clinical
cient end to end data flow and higher quality data.
trials.
ADaM specifies data structures and associated
1.5 Standard for the Exchange of Non-
metadata for analysis datasets. In ADaM, variables,
Clinical Data (SEND)
observations and even entire datasets may be
The SEND model is based on SDTM, and is used
derived to enable the scientific evaluation of the
exclusively to organize, structure, and format non-
objectives of the clinical study. ADaM allows
clinical data from animal toxicology studies sub-
reviewers to have a clear understanding of how the
mitted to the FDA. SEND facilitates transfer of
analysis datasets were derived and how they are to
non-clinical data from the sponsor to the FDA.
be used to produce the statistical results provided
Just like SDTM, observations are arranged into
in the submission.
observation classes: interventions, findings, and
Through standardization of the structures of
special purpose domains. Due to the differing
analysis datasets, software tools can be created
nature of the subject and studies, SEND necessar-
that allow access, manipulation and viewing of anal-
ily has differences in domain contents. There are
ysis datasets. This further increases efficiency to
four categories that describe observation variables:
assist all parties involved in the creation and use of
identifier, topic, timing, and qualifier ̶ identical
analysis datasets. There are 4 key principles of a
to SDTM (Table 1).
well-constructed dataset: 1) facilitate clear and
unambiguous communication and provide a level of
1.6 The Analysis Data Model (ADaM)
traceability, 2) be analysis ready, 3) be linked to
ADaM supports the statistical analysis and sub-
machine-readable metadata in order to facilitate
sequent statistical review of clinical research data.
software development, and 4) work with tools cur-
Analysis datasets and their associated metadata
rently available (Fig. 5).
Table 1 SEND v 2.3 findings domains
Animal Characteristics
Water Consumption
Clinical Signs
Clinical Pathology
Organ Weights
Fetal Data
Group Observations
Drug/Metabolite Levels
Tumor Analysis
Vital Signs
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Food Consumption
Body Weights
Animal Disposition
Macroscopic Findings
Microscopic Findings
Fertility
Group Characteristics
Study Summary
Rodent Micronucleus
Clin Eval 40 Suppl XXXI 2012
Fig. 5 Sample of the CDISC Analysis Data Model (ADaM)
1.7 Protocol Representation Model
Representation standard and also in SDTM. To
(PRM)
assist reviewers in interpreting data received, the
The protocol is the plan or blueprint for the
FDA requested that structured information about
clinical research study. It is the document that
trial design be submitted along with clinical study
describes the objective(s), design, methodology,
datasets. As the TDM began to emerge, it was
statistical considerations and organization of the
quickly realized that trial design has an impact on
trial or study. Ensuring that the protocol is well
both submission data and protocol representation,
designed is vital to the overall success of a research
and the first version of SDTM was released in
study.
2004 including the TDM (or SDM).
CDISC initiated the process of developing the
A Trial Design is a plan for what will be done to
Protocol Representation standard in order to
subjects and what data will be collected about them
support a machine-readable protocol with a set of
in order to address the study s directives as laid
standard elements that can be reused without the
out in the protocol. Key elements of a trial design
need for re-entry. The PRM supports trial regis-
are epochs, arms, study cells, elements, branches,
tration, CRF development, setting up a clinical
treatments, visits, and inclusion/exclusion. Trial
trial management system (CTMS) for managing
designs can be stored as XML and be submitted as
data and tracking studies, and reporting results.
a Trial Design Matrix for viewing by a regulatory
Using the protocol to define data collection tools,
authority. Further information on TDM is avail-
being able to track and readily exchange informa-
able in the SDTM Implementation Guide (SDTM
tion, from CDASH right through to SDTM and
IG) and the Protocol Representation standard.
TDM can be used for the purposes of planning
ADaM, can complete the full life cycle of a research
baselines to support study/project management,
program ̶ a true end to end process.
The Trial Design Model (TDM) or Study
define data collection requirements for a study, and
Design Model (SDM) is at the core of the Protocol
is important to the statistical analysis plan. TDM
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ments (Fig. 6).
allows reviewers to 1) clearly and quickly grasp the
design of a clinical study, 2) compare the designs of
There is now an available XML (ODM) represen-
different studies, 3) search a data warehouse for
tation of the Study Design model. The next step is
clinical studies with certain features, and 4) com-
to develop a tool to help protocol authors (who are
pare planned and actual treatments and visits for
not typically familiar with UML (Unified Modeling
subjects in a clinical study.
Language) models) experience the value of this
standard.
One of the key aims of the Protocol Representation
Group was to ensure that common protocol content
1.8 The Biomedical Research Integrated
can be communicated and exchanged without com-
Domain Group (BRIDG) Model
promising the innovation behind each protocol. The
standard has also been made generic enough to
The BRIDG Model is the combined work of four
accommodate all types of protocols for clinical
major stakeholders: CDISC, HL7 RCRIM, NCI
research, and has utilized existing standards-based
(National Cancer Institute) and FDA. BRIDG
information to guarantee that the Protocol Repre-
effectively enables information system interopera-
sentation standard achieves semantic interoperabil-
bility by harmonizing the CDISC standards to work
ity amongst all systems. There is now a library
together, as well as to ensure that developers can
detailing around 360 common elements that can be
establish applications that will work with the
used repeatedly for new protocols or other docu-
CDISC standards.
ments. The Protocol Representation Model version
The scope of the BRIDG model is ambitious and
1.0 has been represented in the BRIDG model to
vital to the success of future tool development that
express the relationships among the key PR ele-
will work alongside the standards. It is a formal
Fig. 6 Diagram of the sections of the PR Model V1.0 and development steps
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Clin Eval 40 Suppl XXXI 2012
UML Model, the semantic foundation for software
of the CDISC models across the clinical research
applications and message development. The BRIDG
continuum (from Protocol and CDASH through
Model should ensure that systems are able to com-
LAB to SDTM and ADaM). The terminology ini-
municate with each other and transfer data seam-
tiative is a collaboration of volunteers from stake-
lessly. This model can remove the semantic
holders including the FDA, global clinical research
ambiguities present in the complex world of medi-
sponsors, contract research organizations, aca-
cal research by defining the domain semantics,
demia and others. The terminology team also works
serving as the foundation for all standards, soft-
in partnership with the US NCI Enterprise
ware or service specifications in CDISC, HL7,
Vocabulary Services (EVS), as well as actively
NCI and FDA. In order to achieve full semantic
coordinating with ICH, EMA (European Medicines
interoperability, it is essential that healthcare,
Agency), ISO and HL7 RCRIM (Fig. 7).
research and regulatory authorities have a shared
Many companies have worked with proprietary
semantic view. That shared semantic view is
standards for many years and have developed their
BRIDG.
own terminology within their organizations, creating different sets of codes and terms. In order to
1.9 Standard Controlled Terminology
achieve full semantic interoperability across the
Physicians working in the area of medical
standards, the terminology needs to be agreed upon
research are challenged by the various terminolo-
by stakeholders on a global level. To achieve this,
gies/vocabularies that are required. Due to the use
the Standard Controlled Terminology team devel-
of several concurrent codings for healthcare and
oped terminology that supports all CDISC data
medical research, there is a lack of interoperabil-
models, utilized existing open-source terminology,
ity, precipitating a confusing and time-consuming
consulted key industry stakeholders, and collabo-
harmonization process. The CDISC Standard
rated with standards organizations and vocabulary
Controlled Terminology initiative had an initial
developers in order to ensure that terminology has
primary objective to support the terminology needs
international usage. The terminology is currently
Fig. 7 Controlled Terminology broadly supporting semantic interoperability in clinical research
Patient Care
(EHR Systems)
Regulators
(FDA, EMA)
Industry
(Pharma, CROs)
Global Terminology Standards:
(1) an agreed upon definition;
(2) consistency in how they are represented
and “look” electronically; allows for semantic interoperability
NIH & Academia
Comparative
Effectiveness
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maintained through the NCI EVS and is continually
benefits, not least of which is giving physicians the
updated as CDISC continues to develop new stan-
ability to cut down time reentering information
dards, processes and procedures.
written on paper forms and the ability to ensure
standardized fields for entry. An additional, future
1.10 CDISC Glossary
use of the EHR may be the potential for EHR data
The CDISC Glossary was actually the first ini-
to be utilized in the realm of medical research, yet
tiative with demonstrated success within a newly
much of the valuable data in EHRs is not accessed
established CDISC organization. This very useful
for regulated research at this given time. Reaching
CDISC standard dictionary is now updated annu-
the point where research is more closely linked to
ally and published on the CDISC website and in
the workflow of medical professionals is the key
Applied Clinical Trials. It includes dictionary-style
driver behind the CDISC Healthcare Link Initia-
definitions of terms frequently used in clinical
tive.
CDISC is working toward the time when medical
research, along with a set of Abbreviations and
research workflow is seamlessly linked with the
Acronyms used in this arena.
The BRIDG Model, Glossary and Controlled
healthcare workflow. This development will sig-
Terminology all support the end-to-end suite of
nificantly improve recruitment of physicians who
CDISC standards to ensure harmonization and
will conduct research and also patients who will
semantic interoperability across these models (Fig.
participate in research studies, in turn bringing
8).
about better patient care. Some exciting developments in the Healthcare Link Initiative to form a
2．CDISC Innovations
basis for linking research and healthcare are:
1) The Electronic Source Data Interchange
2.1 Healthcare Link Initiative
(eSDI) document ̶ a seminal work on eSource
Electronic Health Records (EHR) have many
aimed at bringing about a paperless process
Fig. 8 The CDISC suite of standards: Protocol through reporting
Global Standards for Clinical Research
(Protocol-driven Research; Protocol
Reporting)
FDA eSubmissions
Aggregation; Analysis/Reporting
Aggre
FDA Critical Path
Initiative
ODM*
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Clin Eval 40 Suppl XXXI 2012
(CRD) that maps CCD to CDASH.
for research.
2) The Biomedical Research Integrated Domain
This approach, which makes use of RFD as a
Group (BRIDG) model ̶ a collaborative
workflow enabler to produce CDASH and other
project using unified modeling language, cre-
standards-based data such as safety information) is
ating a reference map that harmonizes clinical
now being used in other countries around the world
research standards and healthcare standards.
(Fig. 9).
3) Integrating the Healthcare Enterprise (IHE)-
2.2 Shared Health And Clinical Research
CDISC, Retrieve Form for Data Capture
Electronic Library (SHARE)
Integration Profile (RFD) ̶ a real world
implementation that brings EHR closer to the
SHARE is a global, accessible electronic library,
world of medical research through button-
which through advanced technology enables precise
ready technology, just-in-time workflow, and
and standardized data element definitions that can
bite-sized semantics to easily and efficiently
be used in applications and studies to improve bio-
pre-populate CRFs up to 80%.
medical research and its link with healthcare. Built
4) An Interoperability Specification (IS #158)
on BRIDG, SHARE is intended to be a healthcare-
documenting the use of three standards for the
biomedical research enriched data dictionary com-
use case of providing a core research dataset
plemented by detailed clinical content, ISO data
from an EHR. This IS, developed through the
types and terminologies.
HITSP (Healthcare Information Technology
SHARE
will
provide
computable
semantic
Standards Panel) process that was managed
interoperability enabling the reuse of data, and is
through ANSI (American National Standards
structured on four pillars:
Institute) in the U.S., cites the implementation
1. Common information model (BRIDG)
of the Continuity of Care Document (CCD),
2. Strong data typing (ISO 21090)
RFD and CDASH (producing CDASH from
3. Common terminologies/value sets (CDISC,
CCD through a Clinical Research Document
HL7, SNOMED (Systematized Nomenclature
Fig. 9 The approach through which EHRs that are RFD-ready provide CDASH
(core research dataset)
CRD:
Pre-population of a Case Report Form
Healthcare Site
EHR
CRD
XSLT
CCD
Sponsor
CDASH
eCRF
CDMS
A Button-ready, Opportunistic, and
Optimistic Approach
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access the definitions on a 24/7 basis. Perhaps
of Medicine), etc.)
most importantly, SHARE makes it possible to fur-
4. Processes supporting exchange of informa-
ther enable semantic interoperability and provides
tion
a link between research and healthcare.
A Pilot conducted with Mayo Clinic provided
CDISC information upon which SHARE can be
Conclusion
developed (Fig. 10).
SHARE will provide the opportunity to improve
upon data quality through the establishment of bet-
CDISC Standards and Innovations have been
ter definitions, enhancing data consistency to allow
developed to streamline global clinical research
data integration and aggregation, as well as com-
processes. The next steps will include development
parison across research organizations, regulators
of additional domains to support the individual
and partners. This library focuses on reducing the
therapeutic areas. In other words, the efficacy
costs associated with the maintenance of diverse
standards will be added to the safety standards
dictionaries within separate research organiza-
over the coming years. CDISC is made possible
tions, and fosters improved interactions with part-
through the global support of volunteers and orga-
ners through the mutual use of standardized
nizations that participate in CDISC activities, take
definitions. This electronic and collaborative infra-
CDISC educational courses and join CDISC to
structure will speed up the process in developing
demonstrate their interest in improving the pro-
standardized definitions, improve governance of
cesses and quality of medical research for the ben-
those definitions, and provide an outlet in which to
efit of patients everywhere.
Fig. 10 Diagram of SHARE pilot
New
content
(therapeutic
area specific)
Edit, Comment, Annotate
For Harmonization
•SNOMED – CT
•MedDRA (subset)
•NCI Metathesaurus
(BRIDG, CDISC CT, CDASH)
•HL7 V3 Terminology
•UCUM
•ICD – 9 and 10
ISO 21090 datatypes
Hosted Platform (Mayo LexGrid Wiki)
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Forms &
Structure
Alignment
Outcome
Aligned
and
Harmonized
Content
(Draft
Standard)
Procedure
to
Establish
New Standard
(TBD)
“Target”