1. Combined Data Interchange Standard
Consortium (CDISC)
Standard Data Tabulation Model
Presented By:
Ankur Sharma
Biostatistical Programmer
PAREXEL International,
Baltimore, MD, USA

2. Definitions
 CDISC : Clinical Data Interchange Standard Consortium
 SDTM : Standard Data Tabulation Model
 ADaM : Analysis Data Model
 SDS : Submission Data Standards
 DDT : Data Definition Tables

3. Clinical Data Interchange Standard
Consortium (CDISC)
 CDISC is a global, open, multidisciplinary, non-profit
organization that has established standards to support
the acquisition, exchange, submission and archive of
clinical research data and metadata.
 Leads the development of standards that improve
efficiency while supporting the scientific nature of clinical
research.
 Recognizes the ultimate goal of creating regulatory
submissions that allow for flexibility in scientific content
and are easily.

4. Standard Data Tabulation Model
(SDTM)
 SDTM defines a standard structure for study data
tabulations (datasets) that are to be submitted to a
regulatory authority such as the Food & Drug
Administration (FDA).
 Benefits of SDTM:
SDTM allows reviewers at the FDA to develop a
repository of all submitted studies and create stand
alone tools to access, manipulate and view the study
data.

5. SDTM Implementation Guide and Its
Versions
 Study Data Tabulation Model Implementation Guide
(SDMIG) for Clinical Trials is prepared by Submission
Data Standards (SDS) Team.
 Implementation guide has two different versions:
i) SDTMIG V3.1.1
ii) SDTMIG V3.1.2 (newest and preferred)

6. Continued….
 SDTM Implementation guide describes the general
conceptual model for preparing clinical study data that
is submitted to regulatory authorities. The SDTMIG V3.1.2
provides specific domain models, assumptions, business
rules, and examples for preparing standard tabulation
datasets.

7. SDTM Fundamentals
• SDTM Variable Classification:
1.) Identifier : These are the variable which
identifies the study, subject involved,
domain and sequence number.
2.) Topic : This specifies the focus of the observations
3.) Timing : Describes the timing of an observations
4.) Qualifier : Contains additional text, values,
or results which helps describes the
observations

8. Continued….
 5.) Rule: This explains the algorithm or calculation
involved to derived date- times or visits. This is mainly
used in Trial Design Domain.
 Classification of Qualifier Variables:
Qualifier is further categorized into five classes:
1.) Grouping Qualifier : These are used to group together
a collection of observation. Example: LBCAT.
2.) Result Qualifier : This describes the specific result
associated with Topic variables for a finding.

9. Continued….
 3.) Synonym Qualifier: This variable contains alternate
value name for a particular observation. For ex:
AETERM, AEMODIFY and AEDECOD.
 4.) Record Qualifier: This defines additional attributes of
an observations in a record. Ex: AEREL.
 5.) Variable Qualifier: This variable further describes the
value of a observation in a record. Ex: Lab Units
(LBORRESU).

10. Observation Class
Datasets containing observations are classified into three
classes:
 Intervention: This class captures information regarding
investigational treatment, therapeutic treatment and
procedures. Ex: CM, EX, SU.
 Events: This class captures occurrences and incidents
occurred during study trial. Ex: AE, MH, DS, DV.
 Findings: This class captures observation resulting from
planned evaluation. Ex: IE, LB, QS, PE, PC, PP, SC, VS.

11. Special Purpose Domain:
Special Purpose Domain:
 Include subject level data and do not conform to any of
the three classes of observation datasets.
 Examples are:
– Demographics (DM)
– Comments (CO)
– Subject Visits (SV)
– Subject Elements (SE)

12. Trial Design Model
Trial Design: The design of a clinical trial is a plan for what
assessments will be done to subjects and what data will
be collected during the trial to address the trial's
objectives.
 These datasets fall under this model:
– Trial Arms (TA)
– Trial Elements (TE)
– Trial Visits (TV)
– Trial Inclusion/Exclusion Criteria (TI)
– Trial Summary Information (TS)

13. Special Purpose Relationship Datasets
 Supplemental Qualifiers – SUPPQUAL:
Suppqual datasets are used to capture non standard
variables and their association to parent records.
• Relate Records – RELREC:
RELREC is used to describe the relationship between
records in two or more dataset. For ex: Adverse Event
record related to the Concomitant medication.

14. Continued….
 Supplemental Qualifiers – SUPPQUAL:
Supplemental Qualifiers are always created in the
following situation.
1.) Availability of non SDTM standard data which has
study data but cannot be used in the parent domain.
For ex: PE abnormal findings are in SUPPPE

15. Continued….
2.) Any dataset in which SDTM variable has text value
exceeding the length of 200 character limit. Text
value is split such that characters 1- 200 are in the
parent domain and characters >200 go into the
Suppqual domain.
Only exception in this case: Trial Inclusion/Exclusion
(TI) domain. If variable IETEST >200, then the
remaining part of text will go into metadata and will
linked to the Define.xml.

16. Continued….
 RELREC is created only per sponsor’s request for the
following cases.
1.) Information collected about relationship between
concomitant medication and Adverse Event for an
observation.
2.) Any information which has link between
multiple datasets and has a scientific rationale behind
the link.

17. Sponsor Defined or Custom Domain
 These are the domains usually created in any of the
study trial, when we encounter data which is of non
SDTM standard and cannot be included in any of the
SDTM domain. To include this non SDTM standard data
into the study domains, sponsor defined or custom
domains are created.
 Naming of these domains is also sponsor dependent, but
the first letter for these domains assigned according to
their observation class defined on next slide:

18. Continued….
 Intervention = X-
 Events = Y-
 Findings = Z-
 According to the data available, we can decide under
which observation class particular data resides and we
can term the name accordingly.
 For ex: If we have any kind of assessment data involved.
So assessments provides us with finding of new data
information, so dataset can be assigned as ZA.

19. Metadata Contents and Attributes
 Core Variables:
a.) Required: These variable must be present in
the dataset and cannot be null for any record.
b.) Expected: These variable must be present in
the dataset but can have a null value.
c.) Permissible: These variable should be included in
variable appropriately and when data is collected.
If all records have a null value, then this variable
should be dropped.

20. Controlled Terminology
 Controlled Terminology is defined as the terminology
that controls the value of any variable. (See Appendix C,
Page 271 SDTMIG 3.1.2).
 In almost all of SDTM domains, there are some variables
which always have controlled terminology associated
with them. If any variable is defined in the SDTMIG with
the Controlled Terms or Formats as ACN, NY, STERF,
NCOMPLT etc., then all the values of this variable must
be populated using the Controlled terminology.

21. Continued….
TerminologySDTM Terminology.xls
• This file consist of all value of controlled terminology
for SDTM variables and its synonym values. While
creating SDTM domain programmer must check
the value for controlled variable in the file and then
provide responses.
Lab_controlled_terminology.xls
Microsoft Excel
Worksheet
Microsoft Excel
Worksheet

22. Date and Time variable
 --DTC and –STDTC :
All timing variable –DTC and –STDTC variable fall into
either permissible or expected category depending on
dataset. As per definition these variable are allowed to
have null records. As per CDISC guidelines all timing
variable must be presented in ISO 8601 format in all of
the SDTM domain.

23. ISO 8601 Format
• ISO 8601 Format:
As per the FDA guidelines all the dates available in
SDTM dataset must follow the following format for the
date and time presentation in variables such as --DTC
and --STDTC.
YYYY-MM-DDTHH:MM:SS
• Also as per the format, value of this variable must be a
character value.

24. ISO 8601 Duration Values
 In any case possible, where instead of dates and time, we
encountered the value in the following way for example
as shown in the column label Duration recorded then
values must be recorded as shown in the column on right
labeled as –DUR value.
 Duration recorded --DUR value
2 Years P2Y
10 Weeks P10W
3 Months 10 days P3M10D
2 hours before RFSTDTC -PT2H*

25. References:
 www.cdisc.org
 http://www.cancer.gov/cancertopics/terminologyresource
s/CDISC

26. Questions??