SDTM (Study Data Tabulation Model) defines a standard structure for human clinical trial (study) data tabulations and for nonclinical study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA).

1. Study Data Tabulation Model
(SDTM)
Prepared By
Swaroop Kumar

2. 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.
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. CDISC standards are vendor-neutral,
platform-independent and freely available via the CDISC website.
CDISC- Introduction

3. SDTM defines a standard structure for study data tabulations that are to be
submitted as part of a product application to a regulatory authority such as
the United States Food and Drug Administration (FDA)
Data tabulation datasets are one of four ways to represent the human subject
Case Report Tabulation (CRT)
SDTM provides a general framework for describing the organization of
information collected during human and animal studies and submitted to
regulatory authorities.
SDTM- Introduction

4. Protocol CRF
Operational
database
Results
Analysis
datasets
Tabulation
datasets
PRM CDASH ODM
SDTMADaM
Overview of CDISC standards

5. SDTM 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. A collection
of observations on a particular topic is considered a domain.
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.
A Role describes the type of information conveyed by the variable
about each distinct observation and how it can be used.
Model Concept and Terms

6. • Identifier variables, such as those that identify the study, the
subject (individual human or animal or group of individuals)
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).
• 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).
• Rule variables, which express an algorithm or executable method
to define start, end, or looping conditions in the Trial Design model.
Classification Of SDTM Variables

7. The set of Qualifier variables can be further categorized into five sub-classes:
• Grouping Qualifiers are used to group together a collection of observations within the
same domain. Examples include --CAT and --SCAT.
• Result Qualifiers describe the specific results associated with the topic variable in a
Findings dataset. They answer the question raised by the topic variable. Result Qualifiers
are --ORRES, --STRESC, and --STRESN.
• 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 --TEST for --TESTCD.
• 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 all other SAE flag variables in the AE domain; AGE, SEX, and RACE in the DM
domain; and --BLFL, --POS, --LOC, --SPEC, and --NAM in a Findings domain
• Variable Qualifiers are used to further modify or describe a specific variable within an
observation and are only meaningful in the context of the variable they qualify. Examples
include --ORRESU, --ORNRHI, and --ORNRLO, all of which are Variable Qualifiers of --
ORRES; and --DOSU, which is a Variable Qualifier of --DOSE.
Classification Of Qualifier Variables

8. Example:
“Subject 101 had mild nausea starting on Study Day 6” is an
observation belonging to the Adverse Events domain in a clinical
trial.
The Topic variable value is the term for the adverse event,
“NAUSEA”
The Identifier variable is the subject identifier, “101”
The Timing variable is the study day of the start of the event,
which captures the information, “starting on Study Day 6”
Record Qualifier is the severity, the value for which is “MILD”

9. Observations about study subjects are normally collected for all subjects in a
series of domains. A domain is defined as a collection of logically related
observations with a common topic
Each domain dataset is distinguished by a unique, two-character code that
should be used consistently throughout the submission
This code, which is stored in the SDTM variable named DOMAIN, is used in four
ways: as the dataset name, the value of the DOMAIN variable in that dataset, as
a prefix for most variable names in that dataset, and as a value in the RDOMAIN
variable in relationship tables
All datasets are structured as flat files with rows representing observations and
columns representing variables. Each dataset is described by metadata
definitions that provide information about the variables used in the dataset
The metadata are described in a data definition document named “define” that
is submitted with the data to regulatory authorities
Domain

10. Define-XML specifies seven distinct metadata attributes to describe SDTM data:
• The Variable Name (limited to 8 characters for compatibility with the SAS
Transport format)
• A descriptive Variable Label, using up to 40 characters, which should be unique
for each variable in the dataset
• The data Type (e.g., whether the variable value is a character or numeric)
• The set of controlled terminology for the value or the presentation format of the
variable (Controlled Terms or Format)
• The Origin of each variable
• The Role of the variable, which determines how the variable is used in the
dataset. Roles include Identifiers, Topic, Timing, and the five types of Qualifiers.
• 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.

11. The majority of observations collected during a study can be divided among three
general observation classes: Interventions, Events, or Findings:
The Interventions class captures investigational, therapeutic and other treatments that
are administered to the subject (with some actual or expected physiological effect)
either as specified by the study protocol (e.g., “exposure”), coincident with the study
assessment period (e.g., “concomitant medications”), or other substances self-
administered by the subject (such as alcohol, tobacco, or caffeine)
Events class captures planned protocol milestones such as randomization and study
completion, and occurrences, conditions, or incidents independent of planned study
evaluations occurring during the trial (e.g., adverse events) or prior to the trial (e.g.,
medical history).
Findings class captures the observations resulting from planned evaluations to address
specific tests or questions such as laboratory tests, ECG testing, and questions listed on
questionnaires. The Findings class also includes a sub-type “Findings About” which is
used to record findings related to observations in the Interventions or Events class.
General Observation Classes

12. In addition to the three general observation classes, a submission will generally
include a set of other special-purpose datasets of specific standardized structures to
represent additional important information. Examples include the following:
 A Demographics special-purpose domain is included with human and animal
studies
 Other special purpose domains such as Comments , Subject Elements ,Subject
Visits
 Datasets to describe the design of a trial
 Datasets to represent the relationships between datasets and records
Demographics Domain: Demographics is the parent domain for all other
observations for subjects, and should be identified with the domain code of “DM”.
The Demographics domain describes the essential characteristics of the study
subjects, and is used by reviewers for selecting subsets of subjects for analysis. The
Demographics domain, as with other datasets, includes Identifiers, a Topic variable,
Timing variables, and Qualifiers. Since DM has a fixed structure, only certain variables
may be added as appropriate.

13. Comments Domain: Comments are collected during the conduct of many
studies. These are normally supplied by a principal investigator, but might also
be collected from other sources such as central reviewers. When collected,
comments should be submitted in a single Comments domain
The Subject Elements Table:
The Subject Elements table describes the actual order of Elements that were
traversed by the subject, together with the start date/time and end date/time
for each Element. These correspond to the planned Elements described in the
Trial Elements of the Trial Design Model. Because actual data does not always
follow the plan, the model allows for descriptions of an unplanned Element for
subjects.
The Subject Visits Table:
The Subject Visits table describes the actual start and end date/time for each
visit of each individual subject. These correspond to the planned visits
described in the Trial Design Model Trial Visits table. Because actual data does
not always follow the plan, the model allows for descriptions of unplanned visits
for subjects.

14. CDISC SDTM
SDTM Domains (as per Version 3.2)

15. Interventions
CM
EC
EX
Events
AE
CE
DS
Findings
DA
Special-Purpose
CO
DM
SE
SV
SU
PR
DV
HO
MH
DD ECG
IE IS LB
MB MI MO
MS PC PP
PE QS RP
RS SC SS
TU TR VS
Domains
Findings About
FA
SR
Trial Design
TA TD
TE TV
TI TS

16. The Trial Design Model (datasets)
The Trial Design Model defines a standard structure for representing the planned sequence of
events and the treatment plan for the trial. The model provides a standard way to define the
treatment groups and planned visits and assessments that will be experienced by trial subjects.
The model is built upon the concepts of Elements, Arms, Epochs, and Visits. The variables
corresponding to these concepts are used in many domains. The implementation guides define
specific details and examples for Trial Design.
Under the model, planned information is presented in a series of four tables:
• The Trial Elements table describes the Element code (unique for each Element), the Element
description, and the rules for starting and ending an Element. A rule could be expressed as
pseudo code or as executable code for determining transitions from one Element to another.
• The Trial Arms table describes each planned Arm in the trial. An Arm is described as an
ordered sequence of Elements, and the same Element may occur more than once in a given
Arm. In order to accommodate complex Trial Designs, this table allows for rules for branching
from one Element to another when a choice is available, and a rule for transitions to allow a
subject to either skip ahead to another Element rather than proceed linearly.

17. The Trial Visits table describes the planned order and number of visits in the
study. In the case when visits vary for each Arm, there would be a separate
record per Visit per Arm. It describes the allowable or planned values for VISIT,
VISITNUM and VISITDY in the trial (which are subsequently used as Timing
Variables for the collected study data), and rules for starting and ending each
visit. In most blinded trials, the timing of visits is the same for all subjects in all
Arms.
 The Trial Sets table (TX) allows the submission of detailed information about
planned groups of subjects that result as a combination of experimental factors
of interest for a study (including experimental parameters, inherent
characteristics, and sponsor-defined attributes). A Set may be a planned
subdivision of a Trial Arm, or may consist of one or more Trial Arms. These
datasets are essential to determine whether data comparisons are feasible
across different studies.

18. Trial Inclusion/Exclusion Criteria
The Trial Inclusion Exclusion Domain (TI) contains one record for each of the
inclusion and exclusion criteria for the trial.
Trial Summary Information
The Trial Summary Information Domain (TS) contains one record for each trial
summary characteristic. Trial Summary is used to record basic information about the
trial, such as trial phase, protocol title and design objectives.
Trial Disease Assessments
The TD domain provides information on the planned protocol-specified disease
assessment schedule..

19. Trial Visits describes the
planned Visits for each Arm,
and any start and end rules.
Screen Run-In Drug A
Screen Run-In Drug B
Screening
Screen Run-In Placebo
Drug A
Drug B
Trial Arms
describes the
Elements in each
Arm, their order
and Epoch, and
any branching or
transition rules.
Screen Run-in Placebo
Drug A Drug B
Trial Elements describes the
Elements and the rules for
the start and end of each.
Placebo
Run-In Treatment
Epochs are described only in Trial
Arms, and have no separate table.
Visit 1 Visit 2 Visit 3 Visit 4 Visit 5

20. There are many occasions when it is necessary or desirable to
represent relationships among datasets or records. The SDTM identifies
eight distinct types of relationships:
• A relationship between a group of records for a given subject within the
same dataset.
• A relationship between independent records (usually in separate
datasets) for a subject, such as a concomitant medication taken to treat
an adverse event.
• A relationship between two (or more) datasets where records of one
(or more) dataset(s) are related to record(s) in another dataset (or
datasets).
• A dependent relationship where data that cannot be represented by a
standard variable within a general-observation-class dataset record (or
records) can be related back to that record.
• A dependent relationship between a comment in the Comments
domain and a parent record (or records) in other datasets, such as a
comment recorded in association with an adverse event.
• A relationship between a subject and a pool of subjects.
Related Records Dataset

21. Supplemental Qualifiers Dataset
Pool Definition Dataset
This dataset identifies individual subjects included in a pool of
subjects for which a single observation record (pool level) is
captured.
Related Subjects Dataset
Some studies include subjects who are related to each other, and
in some cases it is important to record those relationships. Studies
in which pregnant women are treated and both the mother and her
child(ren) are study subjects are the most common case in which
relationships between subjects are collected. There are also
studies of genetically based diseases where subjects who are
related to each other are enrolled, and the relationships between
subjects are recorded.
Related Records Dataset

22. Overview:
The following standard domains, listed in alphabetical order by Domain
Code, with their respective domain codes have been defined or
referenced by the CDISC SDS Team in this document.
Special-Purpose Domains (defined in Section 5 – Models For
Special-Purpose Domains):
• Comments (CO) • Demographics (DM)
• Subject Elements (SE) • Subject Visits (SV)
Interventions General Observation Class (defined in Section 6.1 -
Interventions):
• Concomitant Medications (CM) • Exposure as Collected (EC)
• Exposure (EX) • Substance Use (SU)
• Procedures (PR)
Events General Observation Class (defined in Section 6.2 -
Events):
• Adverse Events (AE) • Clinical Events (CE) • Disposition (DS) •
Protocol Deviations (DV) • Healthcare Encounters (HO) • Medical

23. Findings General Observation Class (defined in Section 6.3 -
Findings):
• Drug Accountability (DA) • Death Details (DD) • ECG Test Results (EG)
• Inclusion/Exclusion Criterion Not Met (IE)
• Immunogenicity Specimen Assessments (IS)
• Laboratory Test Results (LB) • Microbiology Specimen (MB)
• Microscopic Findings (MI) • Morphology (MO)
• Microbiology Susceptibility Test (MS)
• PK Concentrations (PC) • PK Parameters (PP) • Physical Examination
(PE)
• Questionnaires (QS) • Reproductive System Findings (RP)
• Disease Response (RS) • Subject Characteristics (SC)
• Subject Status (SS) • Tumor Identification (TU) • Tumor Results (TR)
• Vital Signs (VS)
Findings About (defined in Section 6.4 - FA Domain)
• Findings About (FA)
• Skin Response (SR)

24. Trial Design Domains (defined in Section 7 - Trial Design
Datasets):
• Trial Arms (TA)
• Trial Disease Assessment (TD)
• Trial Elements (TE)
• Trial Visits (TV)
• Trial Inclusion/Exclusion Criteria (TI)
Relationship Datasets (defined in Section 8 - Representing
Relationships and Data):
• Supplemental Qualifiers (SUPP-- datasets)
• Related Records (RELREC)
A sponsor should only submit domain datasets that were actually
collected (or directly derived from the collected data) for a given
study. Decisions on what data to collect should be based on the
scientific objectives of the study, rather than the SDTM. Note that
any data that was collected and will be submitted in an analysis

25. Trial Elements (TE) dataset provides descriptions of the
building blocks that make up the study, including what event
indicates their start, what event indicates their end, and their
duration (if they have a fixed length).
Trial Arms (TA) dataset describes the planned treatment
paths (Arms) and the order of the Elements within each of
these
Trial Visits (TV) dataset describes the planned schedule of
Visits. Visits are defined as "clinical encounters" and are
described in the SDTM/SDTMIG using the timing variables,
VISITNUM, VISIT, and VISITDY.
Trial Inclusion/Exclusion (TI) describes the
inclusion/exclusion criteria used to screen subjects. Its
structure is one record per criteria per trial.
Trial Summary (TS) includes key details, called Parameters,
about the trial that would be useful in clinical trials registry.

26. 1. The Variable Name (limited to 8-characters for
compatibility with the SAS system V5 Transport format)
2. A descriptive Variable Label, using up to 40 characters,
which should be unique for each variable in the dataset
3. The data Type (e.g., whether the variable value is a
character or numeric)
4. The set of controlled terminology for the value or the
presentation format of the variable(Controlled Terms or
Format)
5. The Origin or source of each variable
6. The Role of the variable (Identifier, Topic, Timing, or the
five types of Qualifiers)
7. Comments or other relevant information about the
variable or its data 26
Submission metadata model uses seven
distinct metadata attributes

27. 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.

28. 28© 2014 Accenture All Rights Reserved.
Protocol • pdf (i.e., study001-protocol.pdf)
SAP • pdf (i.e., sutdy001-sap.pdf)
eCRF • pdf (i.e., blankcrf.pdf)
SDTM • xpt (i.e., dm.xpt, ae.xpt, and ds.xpt)
ADaM • xpt (i.e., adsl.xpt, adae.xpt, and adtteos.xpt)
SEND • xpt (i.e., dm.xpt, se.xpt, and bw.xpt)
CSR • pdf (i.e., sutdy001-csr.pdf)
Define • xml or pdf (i.e., define.xml/define.pdf)
ADaM SAS
programs
• txt (i.e., c-adsl-sas.txt)
Efficacy SAS
programs
• txt (i.e., t-14-01-001-ds-sas.txt )
SDRG/ADRG • pdf (study001-study-data-reviewer-guide.pdf)
Format of Electronic Files according to
eCTD

29.  The annotated CRF provides the migration team with a
clear presentation of SDTM variables
 Completed following the GAP analysis and prior to
migration
 Experience needed to create the annotated CRF
 Knowledge of the SDTM Implementation Guide and
Metadata Submission Guidelines (chapter 4 – Guidelines
for Annotating CRFs)
Annotated CRF

30.  Upon receiving all required information, start defining
mapping rules.
 The mapping rules should be defined from source
datasets (raw data) to target datasets (SDTM)
 Key components
 SDTM standards and Controlled Terminology files
 Study documents including original Annotated CRF
 Standard template for specifications
 Standard mapping rules to be used (e.g. ISO date
conversion, rename, derive etc.)
Specification

31.  Adapt study terminology to standard CDISC (NCI) terminology
(CT)
 The mapping needs to be described in the define.xml
 CT not only restricted to only those values that are available in
the data
 All entries capitalized for CDISC CT
 ‘Spelling’ as per external dictionary (e.g. MedDRA)
Controlled Terminology
Source CDISC/NCI CT
INFUSION PARENTERAL
Administration by injection, infusion, or implantation
MOUTH/THROAT ORAL
Administration to or by way of the mouth
TOPICAL-EYE OPHTHALMIC
TOPICAL PO TOPICAL
Mapping to CDISC (NCI) Controlled Terminology
e.g. Concomitant Medications route

32. SDRG provides FDA Reviewers with additional context for SDTM datasets
(regulatory submission).
The SDRG is intended to describe SDTM data submitted for an individual study in
the Module 5 clinical section of the eCTD.
The SDRG purposefully duplicates information found in other submission
documentation (e.g. the protocol, clinical study report, define.xml, etc.) in order to
provide FDA Reviewers with a single point of orientation to the SDTM datasets.
Study Data Reviewer’s Guide (SDRG)
Introduction
Protocol Description
Subject Data
Descriptions
Data Conformance
Summary

33. Sections of the reviewer’s guide
Introduction
Purpose This section states the purpose of the SDRG. The SDRG Template
includes standard text.
Acronyms Standard industry acronyms (e.g. MedDRA, LOINC, CDISC, SDTM,
ADaM, etc.) do not need to be documented (optional section)
Study Data Standards
code lists and
Dictionary Inventory
Controlled terminology version(s), and dictionary version(s) used in
the study
Protocol Description
Protocol Number
and Title
Protocol number or identifier, title, and versions included in the
submission
Protocol Design Visual representation or brief textual description of the protocol
design.
Trial Design
Datasets
Trial Design Datasets a list of variables is provided followed by a
explanation of the values assigned to the variables. Most of the
variables follow the sponsor controlled terminology.

34. Subject Data Description
Overview Summary orientation to the datasets containing subject data. study history or
timing, mapping of death information, reference start date etc.,
Annotated
CRFs
Description of notable annotation conventions,
Explanation of data that were not submitted
SDTM
Subject
Domains
overview of the subject-related SDTM domains AND Provide hyperlinks
List all subject-related datasets included in the submission alphabetically by
domain code.
Specify the functional category or categories for each domain.
Indicate if a Supplemental Qualifiers dataset is submitted for the domain
If relationships between the domain and other domains have been described
in RELREC, specify the related domains.
Specify the SDTM Observation Class

36. Data Conformance Summary
Conformance
Inputs
• Reviewed every OpenCDISC findings: Errors, Warning, Info.
• Were sponsor-defined validation rules used to evaluate
conformance?
• Were the SDTM datasets evaluated in relation to define.xml?
Issues Summary summarizes findings from OpenCDISC validation rules and sponsor-
defined validation rules.
Additional
Conformance
Details
This section is not intended to contain the full OpenCDISC Details
report. Sponsors are discouraged from submitting the full report, but if
the sponsor considers it necessary, the full report may be submitted as
SDRG

37. Since becoming the recommended standard for the
submission of clinical and preclinical trial data to the FDA in
marketing applications, the SDTM has begun to be used by
sponsors for their upstream processing to support clinical
study reports and integrated reports.
As data is increasingly delivered to the FDA in the CDISC
standards, the FDA will be able to more efficiently review
individual submissions and do ad hoc analyses across
companies.
Conclusion