Clin Eval 40 Suppl XXXI 2012

CDISC as the global standards

CDISC standards and innovations ＊Andrea VadakinRebecca 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 tostreamline medical research and ensure a link with healthcare. The CDISC mission is to develop andsupport 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 andsafety 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 ofthe 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 17organizations across the industry to was formed to

The CDISC standards are summarized, begin-oversee the development of CDASH. The primaryning 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 andby LAB for laboratory data, SDTM, SEND, andregulatory 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 toin 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 arethis publication. The end-to-end support for medi-

closely related, they have differing purposes, spe-cal research of any type should become apparent ascifically submission (SDTM) versus data collection

these standards are envisioned to work together,(CDASH). Basic data collection fields identified bybuilding quality in from the start of a study.

CDASH project teams are mapped into SDTM andare 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 wasdata collection to improve data interoperability andoriginally designed for the development of stan-quality. This is accomplished by focusing on thedards 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 withated 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 improvesystems. 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 thetronic, 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 andstandardized case report form content, as the global

improve communication and efficiency throughout a−2−

Clin Eval 40 Suppl XXXI 2012

Fig. 1hence CDISC has developed ODM to represent

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 aneed 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 clinicalstudy process. It has a single format, providing allcomponents needed to describe clinical researchdata with attribution requirements mandated byregulatory agencies. It reduces the number ofstudy, through the collection of quality data fromunique file formats a clinical application needs tothe studyʼs 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 ofgram, since data are collected in essentially theclinical 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 awhere 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 clinicalresearch. 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 FDAʼs 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 electronicable 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 theXML 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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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 thein the FDAʼs 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 Clinicaldata is that which is collected from laboratories.

Research Information Management) in 2011 due toThrough 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. Thechange 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 typicallydependent 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 withstandards 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 toboth 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−4−

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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, theSDTM 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 thethrough CRFs/CDASH). Each observation isareas needed for submission. Most observations

described by a series of variables, corresponding tocollected during a human clinical study fall into one

a row in a dataset or table. The metadata for eachof three general classes: interventions, events, orvariable includes a variable name, label, formats orfindings. Precise definitions of these three terms,

decodes and origin. These variables are classified

as well as additional Special Purpose domains caninto 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 organizedacross studies (e.g. in data warehouses) and forinto a series of domains consisting of groups of

exchanging/sharing study data across partners, forrelated data, such as adverse events or concomitantexample, 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 tabulationor when the need for aggregation occurs; however,datasets, subject profiles, and listings. SDTM

this is inefficient and there is a loss of quality anddatasets are intended to represent the collected

integrity in the data. It is much more efficient toclinical 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 clinicalcient 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 usedderived 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 themitted to the FDA. SEND facilitates transfer ofanalysis datasets were derived and how they are tonon-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, andThrough standardization of the structures of

special purpose domains. Due to the differing

analysis datasets, software tools can be creatednature of the subject and studies, SEND necessar-that allow access, manipulation and viewing of anal-

ily has differences in domain contents. There areysis datasets. This further increases efficiency tofour categories that describe observation variables:

assist all parties involved in the creation and use ofidentifier, topic, timing, and qualifier ̶ identicalanalysis datasets. There are 4 key principles of a

to SDTM (Table 1).

well-constructed dataset: 1) facilitate clear andunambiguous 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−6−

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, theThe protocol is the plan or blueprint for the

FDA requested that structured information aboutclinical research study. It is the document thattrial 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 thequickly realized that trial design has an impact on

trial or study. Ensuring that the protocol is wellboth submission data and protocol representation,

designed is vital to the overall success of a researchand the first version of SDTM was released instudy.

2004 including the TDM (or SDM).

CDISC initiated the process of developing the

A Trial Design is a plan for what will be done toProtocol Representation standard in order tosubjects and what data will be collected about them

support a machine-readable protocol with a set ofin order to address the studyʼs directives as laid

standard elements that can be reused without theout in the protocol. Key elements of a trial designneed for re-entry. The PRM supports trial regis-

are epochs, arms, study cells, elements, branches,

tration, CRF development, setting up a clinicaltreatments, visits, and inclusion/exclusion. Trial

trial management system (CTMS) for managing

designs can be stored as XML and be submitted asdata 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 andIG) and the Protocol Representation standard.
TDM can be used for the purposes of planning

ADaM, can complete the full life cycle of a researchbaselines 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, andDesign 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 thedesign of a clinical study, 2) compare the designs of

There is now an available XML (ODM) represen-different studies, 3) search a data warehouse fortation of the Study Design model. The next step isclinical studies with certain features, and 4) com-

to develop a tool to help protocol authors (who arepare planned and actual treatments and visits fornot typically familiar with UML (Unified Modeling

subjects in a clinical study.

Language) models) experience the value of thisstandard.

One of the key aims of the Protocol Representation
Group was to ensure that common protocol content
can be communicated and exchanged without com-

1.8 The Biomedical Research Integrated

promising the innovation behind each protocol. The

Domain Group (BRIDG) Model

standard has also been made generic enough to

The BRIDG Model is the combined work of four

accommodate all types of protocols for clinicalmajor 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 candetailing around 360 common elements that can beestablish applications that will work with theused 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 tovital 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−8−

Clin Eval 40 Suppl XXXI 2012

UML Model, the semantic foundation for softwareof the CDISC models across the clinical research

applications and message development. The BRIDG

continuum (from Protocol and CDASH throughModel 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 researchambiguities 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 Enterpriseware 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 Medicinesinteroperability, 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 isstandards 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 andoped 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 initialdevelopers in order to ensure that terminology has

primary objective to support the terminology needsinternational 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, future1.10 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 usefulfor regulated research at this given time. Reaching

CDISC standard dictionary is now updated annu-

the point where research is more closely linked toally and published on the CDISC website and in

the workflow of medical professionals is the key

Applied Clinical Trials. It includes dictionary-styledriver behind the CDISC Healthcare Link Initia-

definitions of terms frequently used in clinicaltive.
CDISC is working toward the time when medical

research, along with a set of Abbreviations andresearch workflow is seamlessly linked with theAcronyms used in this arena.
The BRIDG Model, Glossary and Controlled

healthcare workflow. This development will sig-

Terminology all support the end-to-end suite ofnificantly improve recruitment of physicians who

CDISC standards to ensure harmonization andwill conduct research and also patients who willsemantic 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. 8The 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

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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 aGroup (BRIDG) model ̶ a collaborative

workflow enabler to produce CDASH and otherproject using unified modeling language, cre-standards-based data such as safety information) is

ating a reference map that harmonizes clinicalnow 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, andand standardized data element definitions that canbite-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 thebiomedical research enriched data dictionary com-

use case of providing a core research dataset

plemented by detailed clinical content, ISO datafrom an EHR. This IS, developed through thetypes and terminologies.

HITSP (Healthcare Information Technology

SHARE

will

providecomputablesemantic

Standards Panel) process that was managed

interoperability enabling the reuse of data, and isthrough 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. 9The approach through which EHRs that are ʻRFD-readyʼ provide CDASH(core research dataset)
CRD:
Pre-population of a Case Report Form

Healthcare Site
EHR

CRD
XSLT
CCD

Sponsor
CDASH
eCRF
CDMS

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access the definitions on a 24/7 basis. Perhapsof 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 beConclusion

developed (Fig. 10).
SHARE will provide the opportunity to improveupon 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 researchdata integration and aggregation, as well as com-

processes. The next steps will include development

parison across research organizations, regulatorsof additional domains to support the individual

and partners. This library focuses on reducing thetherapeutic 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, takedefinitions. This electronic and collaborative infra-

CDISC educational courses and join CDISC tostructure will speed up the process in developing

demonstrate their interest in improving the pro-standardized definitions, improve governance ofcesses and quality of medical research for the ben-those definitions, and provide an outlet in which toefit of patients everywhere.

Fig. 10

Diagram of SHARE pilotNewcontent(therapeuticarea 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)− 12 −

Forms &
Structure
Alignment
OutcomeAlignedandHarmonizedContent(DraftStandard)

ProceduretoEstablish
New Standard(TBD)“Target”