SDTM (Study Data Tabulation Model) defines a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting of human clinical trial data tabulations and for non-clinical study data tabulations which are to be submitted as part of a product application(IND and NDA) to a regulatory authority such as the United States Food and Drug Administration (FDA) and PMDA (Japan)

1. INTRODUCTION TO SDTM (STUDY
DATA TABULATION MODEL)
--Devender Palsa

2. What is SDTM?
 SDTM (Study Data Tabulation Model) is a CDISC standard which defines a standard
for organizing and formatting data to streamline processes in collection,
management, analysis and reporting of human clinical trial data tabulations and for
non-clinical study data tabulations which are to be submitted as part of a product
application(IND and NDA) to a regulatory authority such as the United States Food
and Drug Administration (FDA) and PMDA (Japan)
 The Submission Data Standards(SDS) team of Clinical Data Interchange Standards
Consortium (CDISC) defines SDTM

3. Scope of SDTM
 SDTM implementation supports data aggregation and warehousing, promote mining
and re-use, facilitates sharing and helps to perform due diligence and other
important data review activities and improves the regulatory review and drug
approval process
 The availability of standard submission data will give many benefits to
regulatory reviewers. Reviewers will be trained in the principles
of standardised datasets and the use of standard software tools and thus be able to
work with the data more effectively with less preparation time
 Study Data Tabulation Model is also used in medical devices, non-clinical data
(SEND), and pharmaco-genomics/genetics studies
 Clinical trial data review process has significantly accelerated by SDTM standards by
reducing Non-clinial programming (to familiarize data structure, check data
accuracy, formats) time up to 70%

4. Key Components of the SDTM model
 Observations and Variables
 Datasets and Domains
 Special-Purpose Datasets
 The General Observation Classes

5. Observations and Variables
 Each observation can be described by a series of variables, corresponding to a row in a
dataset, or table and each variable can be classified based on its Role
 A Role determines the type of information conveyed by the variable used about each distinct
observation and how it can be used
 Variables can be classified into five major Roles
a. Identifier variables : which identify the study, subject, domain and sequence number of
the record. Example: STUDYID, USUBJID
b. Topic variables: which specify the focus of the observation. Example: AETERM, EXTRT
c. Timing variables: which describe the timing of the observation (start date(CMSTDTC) and
end date(CMENDTC))
d. 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). Example: CMCAT, CMDOSE, CMDOSU
e. Rule variables: which express an algorithm, or executable method to define start, end,
and branching, or looping conditions in the Trial Design model. Example: TABRANCH,
TATRANS

6. Observations and Variables
 There are three categories of variables in the core column of domain models
1. Required: Required must contain variables and variable values are required to
make a record useful in the context of a specific domain
2. Expected: Expected variables are required but variable values are not
mandatory
3. Permissible: Variables and variable values are not mandatory, if collected or
derived should be used in a domain as appropriate

7. Datasets and Domains
 A domain is a collection of logically related observations with a common topic. An
observations about study subjects are collected for all subjects in a series of
domains
 Every domain is represented by a single dataset and each domain dataset is
distinguished by a unique, two-character code(DOMAIN) that should be used
consistently throughout the submission
 Every dataset is described by metadata definitions that provide information about
the variables used in the dataset.

8. Datasets and Domains
 According to Define-XML, seven distinct metadata attributes to describe SDTM data
1. The Variable Name
2. A descriptive Variable Label
3. The data Type
4. The set of controlled terminology for the value or the presentation format of
the variable (Codelist, Controlled Terms, or Format
5. The Origin of each variable
6. The Role of the variable, which determines how the variable is used in the
dataset(Roles are used to represent the categories of variables such as
Identifier, Topic, Timing, or the five types of Qualifiers)
7. Comments or other relevant information about the variable or its data
included by the sponsor as necessary to communicate information about the
variable or its contents to a regulatory agency

9. Special-Purpose Datasets
The SDTM includes three types of special purpose datasets
 Domain datasets, consisting of Demographics (DM), Comments (CO), Subject
Elements (SE), and Subject Visits (SV), all of which include subject-level data that do
not conform to one of the three general observation classes
 Trial Design Model (TDM) datasets like Trial Arms (TA) and Trial Elements (TE), which
provide information about the study design but do not contain any subject data
 Relationship datasets which include the Related Records (RELREC) and
Supplemental Qualifiers: SUPP — Datasets (SUPP)

10. The General Observation Classes
 There are three SDTM General Observation Classes: Interventions, Events, Findings
 Interventions: Anything taken by the subject, which captures investigational,
therapeutic and other treatments that are administered to the subject, coincident
with the study assessment period (e.g: concomitant medications), or self-
administered by the subject (such as use of alcohol, tobacco, or caffeine).
CM, EC, EX, SU, PR
 Events: Anything happened to the subject and captures planned protocol milestones
such as randomization and study completion details, and occurrences, conditions or
incidents independent of planned study evaluations occurring during the study (e.g:
adverse events), or prior to the trial (e.g: medical history)
AE, CE, DS, DV, HO, MH
 Findings: Anything taken from the subject and which captures the observations
resulting from planned evaluations to address specific tests, or questions such as
laboratory tests(LB), ECG testing(EG), and questions listed on questionnaires(QS)

11. Required documents to create a SDTM datasets
 Annotated CRF (aCRF)
 Mapping Specifications
 Raw datasets
 Controlled terminology is a set of code lists and valid values like ISO 8601 format to
represent date/time variables
 SDTM IG
 Protocol and Sponsor defined study specific documents

12. References
 An introduction to CDISC-SDTM
 CDISC-SDTM
 Introduction to Clinical Trials

13. THANK YOU
“ACTIONS DO NOT CLING TO ME BECAUSE I AM
NOT ATTACHED TO THEIR RESULTS. THOSE WHO
UNDERSTAND THIS AND PRACTICE IT LIVE IN
FREEDOM”
--BHAGAVAD GITA
-- Devender Palsa