The document describes CDISC's Study Data Tabulation Model (SDTM), which provides a fundamental model for organizing clinical trial data based on observations of discrete pieces of information (variables). SDTM defines general classes of observations (Events, Findings, Interventions) and variable roles including topic, identifier, timing, and qualifier variables. It discusses CDISC SDTM domains as SAS dataset implementations with optimizations for data exchange and medical review. Controlled terminologies and code lists are also described, as well as the define.xml specification for machine-readable metadata submission.

1. CDISC submission standard
• CDISC SDTM
unfolding the core model that is the basis
both for the specialised dataset templates
(SDTM domains) optimised for medical
reviewers
• CDISC Define.xml
metadata describing the data exchange
structures (domains)

2. Background: CDISC SDTM’s fundamental
model for organizing clinical data
Observation
Generic structure
•Unique identifiers
•Topic variable or parameter
•Timing Variables
•Qualifiers.
Interventions
Findings
Events
General classes
Subject
CM
EX
EG
IE
LB
PE
AE
DS
SDTM Domains
(dataset structures)
…
The patient/subject focused information model of the clinical ‘reality’ (general classes of
observations on subjects: interventions, findings, events). This model has been developed by
CDISC/SDS team and exist today only as a text description.

3. * New in Version 3
Interventions Events
ConMeds
Exposure AE
MedHist
Disposition
Findings
ECG
PhysExam
Labs
Vitals
Demog
Other
Subj Char*
Subst Use*
Incl Excl*
RELATES*
SUPPQUAL*
Study Sum*
Study Design*
QS*, MB*
Comments*
CP*, DV*
CDISC SDTM’s Domains
From CDISC SDTM Overview & Impact to AZ, 2004, by Dan Godoy, presented
at the first CDISC/SDM meeting 20 October 2004

4. Basic Concepts in CDISC SDTM
Observations and Variables
• The SDTM provides a general framework for describing the
organization of information collected during human and animal
studies.
• The model is built around the concept of observations, which
consist of discrete pieces of information collected during a study.
Observations normally correspond to rows in a dataset.
• Each observation can be described by a series of named
variables. Each variable, which normally corresponds to a
column in a dataset, can be classified according to its Role.
• Observations are reported in a series of domains, usually
corresponding to data that were collected together. A domain is
defined as a collection of observations with a topic-specific
commonality about a subject.
From the Study Data Tabulation Model document

5. Basic Concepts in CDISC/SDTM
Variable Roles
• A Role determines the type of information conveyed by the variable
about each distinct observation and how it can be used.
– A common set of Identifier variables, which identify the study, the subject
(individual human or animal) involved in the study, the domain, and the
sequence number of the record.
– Topic variables, which specify the focus of the observation (such as the
name of a lab test), and vary according to the type of observation.
– A common set of Timing variables, which describe the timing of an
observation (such as start date and end date).
– Qualifier variables, which include additional illustrative text, or numeric
values that describe the results or additional traits of the observation (such
as units or descriptive adjectives). The list of Qualifier variables included
with a domain will vary considerably depending on the type of observation
and the specific domain
– Rule variables, which express an algorithm or executable method to define
start, end, or looping conditions in the Trial Design model.
From the Study Data Tabulation Model document

6. Example: Mapping Vital Signs
From CDISC End to End Tutorial - DIAAmsterdam 7 Nov 2004, Pierre-Yves Lastic, Sanofi-
Aventis and Philippe Verplancke, CRO24

7. CDISC’s Submission standard
• Underlying Models:
CDISC Study Data Tabulation Model
Clinical Observations
• General Classes: Events, Findings, Interventions
– Trial Design Model
• Elements, Arms, Trial Summary Parameters etc.
• Domains, submission dataset templates:
CDISC SDTM Implementation Guide

8. CDISC SDTM fundamental model for organizing data collected in
clinical trials
Concept of Observations, which consist of discrete pieces of information
collected during a study described by a series of named variables.
General Classes of Observations: Events, Findings, Interventions
Variable Roles: determines the type of information conveyed by the
variable about each distinct observation: Topic variables, Identifier
variables, Timing variables, Rule variables, and Qualifiers (Grouping,
Result, Synonym, Record, Variable)
General principles and standards

9. CDISC SDTM fundamental model for organizing data collected in
clinical trials
Concept of Observations, which consist of discrete pieces of information
collected during a study described by a series of named variables.
General Classes of Observations: Events, Findings, Interventions
Variable Roles: determines the type of information conveyed by the
variable about each distinct observation: Topic variables, Identifier
variables, Timing variables, Rule variables, and Qualifiers (Grouping,
Result, Synonym, Record, Variable)
General principles and standards
CDISC SDTM Domains
SAS Dataset implementations
(dataset templates)
e.g. Vital Signs domains
Optimisations for Data Exchange per
study and for Medical Reviewers to
easier understand data
Specific principles and standards such
as ISO8601 for dates/timings, and both
Original & Standard values expected

10. CDISC SDTM fundamental model for organizing data collected in
clinical trials
Concept of Observations, which consist of discrete pieces of information
collected during a study described by a series of named variables.
General Classes of Observations: Events, Findings, Interventions
Variable Roles: determines the type of information conveyed by the
variable about each distinct observation: Topic variables, Identifier
variables, Timing variables, Rule variables, and Qualifiers (Grouping,
Result, Synonym, Record, Variable)
General principles and standards
CDISC SDTM Domains
SAS Dataset implementations
(dataset templates)
e.g. Vital Signs domains
Optimisations for Data Exchange per
study and for Medical Reviewers to
easier understand data
Specific principles and standards such
as ISO8601 for dates/timings, and both
Original & Standard values expected
Identifiers of records
per dataset and study
Decoded format, that is, the
textual interpretation of
whichever code was
selected from the code list.

11. CDISC SDTM fundamental model for organizing data collected in
clinical trials
Concept of Observations, which consist of discrete pieces of information
collected during a study described by a series of named variables.
General Classes of Observations: Events, Findings, Interventions
Variable Roles: determines the type of information conveyed by the
variable about each distinct observation: Topic variables, Identifier
variables, Timing variables, Rule variables, and Qualifiers (Grouping,
Result, Synonym, Record, Variable)
General principles and standards
Optimisations for Data Exchange per
study and for Medical Reviewers to
easier understand data
Specific principles and standards such
as ISO8601 for dates/timings, and both
Original & Standard values expected
CDISC SDTM Domains
SAS Dataset implementations
(dataset templates)
e.g. Vital Signs domains
Controlled Terminologies
CT Packages for SDTM
e.g. Codelist Patient
Positiion and proposed
terms for VSTESTCD

12. CDISC SDTM fundamental model for organizing data collected in
clinical trials
Concept of Observations, which consist of discrete pieces of information
collected during a study described by a series of named variables.
General Classes of Observations: Events, Findings, Interventions
Variable Roles: determines the type of information conveyed by the
variable about each distinct observation: Topic variables, Identifier
variables, Timing variables, Rule variables, and Qualifiers (Grouping,
Result, Synonym, Record, Variable)
General principles and standards
Optimisations for Data Exchange per
study and for Medical Reviewers to
easier understand data
Specific principles and standards such
as ISO8601 for dates/timings, and both
Original & Standard values expected
CDISC SDTM Domains
SAS Dataset implementations
(dataset templates)
e.g. Vital Signs domains
Controlled Terminologies
CT Packages for SDTM
e.g. Codelist Patient
Positiion and proposed
terms for VSTESTCD
CDISC Codelist Specification CDISC Codelist Values
Codelist_Name Controlled Terms Comment
Codelist_Name VSTEST --TESTCD --TEST
Codelist_Label Vital Signs Test Name VSTEST WEIGHT Weight
Upper_Case Y VSTEST HEIGHT Height
Restriction_8char Y VSTEST HR Heart Rate
Extensible_NY Y VSTEST PULSE Pulse Rate
Reference_Description Organization Name: CDISC
Document Title: Study Data Tabulation Model
Implementation Guide: Human Clinical Trials
Document Version: 1.01
Date: 2004-07-14
Chapter:10.3.3 Vital Signs Test Codes
Page: 169
VSTEST SYSBP Systolic Blood Pressure
Reference_URL http://www.cdisc.org/models/sds/v3.1/index.html VSTEST DIABP Diastolic Blood Pressure
VSTEST RESP Respiratory Rate
VSTEST TEMP Temperature
VSTEST FRMSIZE Frame Size SIZECD
VSTEST BMI Body Mass Index
VSTEST BSA Body Surface Area
VSTEST BODYFAT Body Fat
VSTEST MAP Mean Arterial Pressure
CDISC Codelist Specification CDISC Codelist Values
Codelist_Name VSRESU Codelist_Name Controlled Terms Comment
Codelist_Label Units for Vital Signs Results VSRESU kg
Upper_Case N VSRESU lb
Restriction_8char N VSRESU cm
Extensible_NY Y VSRESU in
Reference_Description Not applicable VSRESU mmHG
Reference_URL Not applicable VSRESU beats/min
VSRESU kg/m2
VSRESU m2
VSRESU C
VSRESU F
VSRESU breath/min
VSRESU g
VSRESU %
VSRESU Ohm
CDISC Codelist Specification CDISC Codelist Values
Codelist_Name RACE Codelist Controlled Terms Comment
Codelist_Label Race RACE AMERICAN INDIAN OR ALASKA NATIVE FDA code
Upper_Case Y RACE ASIAN FDA code
Restriction_8char N RACE BLACK OR AFRICAN AMERICAN FDA code
Extensible_NY N RACE NATIVEHAWAIIAN OR OTHER PACIFIC ISLANDER FDA code
Reference_Description Organization Name: FDA
Document Title: Draft Guidance for Industry Collection of
Race and Ethnicity Data in Clinical Trials
Date: January 2003
Chapter: III COLLECTING RACEAND ETHNICITY DATA IN
CLINICAL TRIALS
Codelist_Name: Race
Page: 5
RACE WHITE FDA code
Reference_URL http://www.fda.gov/cder/guidance/5054dft.pdf

13. CDISC SDTM fundamental model for organizing data collected in
clinical trials
Concept of Observations, which consist of discrete pieces of information
collected during a study described by a series of named variables.
General Classes of Observations: Events, Findings, Interventions
Variable Roles: determines the type of information conveyed by the
variable about each distinct observation: Topic variables, Identifier
variables, Timing variables, Rule variables, and Qualifiers (Grouping,
Result, Synonym, Record, Variable)
General principles and standards
Optimisations for Data Exchange per
study and for Medical Reviewers to
easier understand data
Specific principles and standards such
as ISO8601 for dates/timings, and both
Original & Standard values expected
CDISC SDTM Domains
SAS Dataset implementations
(dataset templates)
e.g. Vital Signs domains
define.xml
Case Report Tabulation Data Definition Specification
to submit the Data Definition Document (submission
dataset metadata) in a machine-readable format
Controlled Terminologies
CT Packages for SDTM
e.g. Codelist Patient
Positiion and proposed
terms for VSTESTCD

14. CDISC SDTM fundamental model for organizing data collected in
clinical trials
Concept of Observations, which consist of discrete pieces of information
collected during a study described by a series of named variables.
General Classes of Observations: Events, Findings, Interventions
Variable Roles: determines the type of information conveyed by the
variable about each distinct observation: Topic variables, Identifier
variables, Timing variables, Rule variables, and Qualifiers (Grouping,
Result, Synonym, Record, Variable)
General principles and standards
Optimisations for Data Exchange per
study and for Medical Reviewers to
easier understand data
Specific principles and standards such
as ISO8601 for dates/timings, and both
Original & Standard values expected
CDISC SDTM Domains
SAS Dataset implementations
(dataset templates)
e.g. Vital Signs domains
define.xml
Case Report Tabulation Data Definition Specification
to submit the Data Definition Document (submission
dataset metadata) in a machine-readable format
Controlled Terminologies
CT Packages for SDTM
e.g. Codelist Patient
Positiion and proposed
terms for VSTESTCD
CRTDDS = Case Report Tabulation Data Description
Specification (= an ODM extension, formerly
called „define.xml“) will replace define.pdf in e-CTD
ItemGroup
ItemGroup
ItemGroup
Item Item ValueList
Item
Item
Item
(in an item)
<ItemDef OID="SU.SUTRT.SMKCLASS" Name="SMKCLASS" DataType="integer" Length="8“
Origin="CRF Page" Comment="Substance Use CRF Page 4" def:Label="Smoking classification">
<CodeListRef CodeListOID="SMKCLAS" />
</ItemDef>
<CodeList OID="SMKCLAS" Name="SMKCLAS" DataType="integer">
<CodeListItem CodedValue="1">
<Decode>
<TranslatedText xml:lang="en">NEVER SMOKED</TranslatedText>
</Decode>
</CodeListItem>
<CodeListItem CodedValue=“2">
<Decode>
<TranslatedText xml:lang="en">SMOKER</TranslatedText>
</Decode>
</CodeListItem>
<CodeListItem CodedValue=“3">
<Decode>
<TranslatedText xml:lang="en">EX SMOKER</TranslatedText>
</Decode>
</CodeListItem>
define.XML as machine-readable replacement for define.pdf
(= prevoius called Data Defintion Tables in item 11)
> Needs complete syntax to reference external lists
From Randy Levins presentation, see
http://www.cdisc.org/publications/interchange2005/se
ssion8/JANUS2005.pdf
> And to reference sponsor defined code lists cross studies

15. CDISC SDTM fundamental model for organizing data collected in
clinical trials
Concept of Observations, which consist of discrete pieces of information
collected during a study described by a series of named variables.
General Classes of Observations: Events, Findings, Interventions
Variable Roles: determines the type of information conveyed by the
variable about each distinct observation: Topic variables, Identifier
variables, Timing variables, Rule variables, and Qualifiers (Grouping,
Result, Synonym, Record, Variable)
General principles and standards
Optimisations for Data Exchange per
study and for Medical Reviewers to
easier understand data
Specific principles and standards such
as ISO8601 for dates/timings, and both
Original & Standard values expected
CDISC SDTM Domains
SAS Dataset implementations
(dataset templates)
e.g. Vital Signs domains
SDTM fundemantal mode is also the basis for:
• SEND Domains for Nonclinical Data (generated
from animal toxicity studies)
• Future domains of derived data, capturing
metadata to describe derivations and analyses.

16. Basic Concepts in CDISC/SDTM
Subclasses of Qualifiers
• Grouping Qualifiers are used to group together a collection of observations within the
same domain.
– Examples include --CAT, --SCAT, --GRPID, --SPEC, --LOT, and --NAM. The latter three grouping qualifiers can
be used to tie a set of observations to a common source (i.e., specimen, drug lot, or laboratory name,
respectively).
• Synonym Qualifiers specify an alternative name for a particular variable in an
observation.
– Examples include --MODIFY and --DECOD, which are equivalent terms for a --TRT or --TERM topic variable,
and --LOINC which is an equivalent term for a --TEST and --TESTCD.
• Result Qualifiers describe the specific results associated with the topic variable for a
finding. It is the answer to the question raised by the topic variable.
– Examples include --ORRES, --STRESC, and --STRESN.
• Variable Qualifiers are used to further modify or describe a specific variable within an
observation and is only meaningful in the context of the variable they qualify.
– Examples include --ORRESU, --ORNHI, and --ORNLO, all of which are variable qualifiers of --ORRES: and --
DOSU, --DOSFRM, and --DOSFRQ, all of which are variable qualifiers of --DOSE. observation and is
• Record Qualifiers define additional attributes of the observation record as a whole
(rather than describing a particular variable within a record).
– Examples include --REASND, AESLIFE, and allother SAE flag variables in the AE domain; and --BLFL, --POS
and --LOC.
From the Study Data Tabulation Model document

17. Basic Concepts in CDISC/SDTM
Variable Roles
• Topic variables
which specify the focus of the
observation (such as the name of
a lab test), and vary according to
the type of observation.
From the Study Data Tabulation Model document
Topic
Grouping
Qual
Synonym
Qual
• Grouping qualifiers
are used to group together a collection of observations
within the same domain.
– Examples include --CAT, --SCAT, --GRPID, --SPEC, --LOT,
and --NAM. The latter three grouping qualifiers can be used
to tie a set of observations to a common source (i.e.,
specimen, drug lot, or laboratory name, respectively)
• Synonym Qualifiers
specify an alternative name for a particular variable in
an observation.
– Examples include --MODIFY and --DECOD, which are
equivalent terms for a --TRT or --TERM topic variable,
and --LOINC which is an equivalent term for
a --TEST and --TESTCD.
Observation
Record
Qualifier
variables

18. Basic Concepts in CDISC/SDTM
Variable Roles
• Identifier variables
which identify the study, the subject
(individual human or animal) involved
in the study, the domain, and the
sequence number of the record.
• Timing variables
which describe the timing of an
observation (such as start date and
end date).
From the Study Data Tabulation Model document
Identifier Timing
• Result Qualifiers
describe the specific results associated
with the topic variable for a finding. It is the
answer to the question raised by the topic
variable. Depending on the type of result
(numeric or character) different variables
are being used. Includes variables for both
original (as supplied values) and for
standardised values (for uniformity).
– Examples include --ORRES,
--STRESC, and --STRESN.
Observation
Record
Qualifier
variables
Topic
Result
Qual

19. Basic Concepts in CDISC/SDTM
Variable Roles
From the Study Data Tabulation Model document
Identifier Timing
• Variable Qualifiers
are used to further modify or describe a specific
variable within an observation and is only
meaningful in the context of the variable they
qualify.
– Examples include --ORRESU, --ORNHI,
and --ORNLO, all of which are variable
qualifiers of --ORRES: and --DOSU, --
DOSFRM, and --DOSFRQ, all of which are
variable qualifiers of --DOSE.
– Indictors where the results falls with respect
to reference range
Observation
Record
Qualifier
variables
Result
Qual
Variable
Qual
Topic

20. Basic Concepts in CDISC/SDTM
Variable Roles
From the Study Data Tabulation Model document
Identifier Timing
• Record Qualifiers
define additional attributes of the observation
record as a whole (rather than describing a
particular variable within a record).
– Examples include --REASND, AESLIFE, and
allother SAE flag variables in the AE domain; and
--BLFL, --POS and --LOC.
Observation
Record
Qualifier
variables
Result
Qual
Variable
Qual
Record
Qual
Topic

21. Basic Concepts in CDISC/SDTM
Subclasses of Qualifiers
• Topic variables
• Identifier variables
• Timing variables
• Rule variables
From the Study Data Tabulation Model document
Topic Identifier Timing
Grouping
Qual
Synonym
Qual
• Qualifier variables
– Grouping Qualifiers
– Result Qualifiers
– Synonym Qualifiers
– Record Qualifiers
– Variable Qualifiers
Record
Qual
Observation Record
Result
Qual
Variable
Qual