1. STUDY DATA
TABULATION
MODEL
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2. • SDTM (Study Data Tabulation Model) defines a standard structure
for human clinical trial (study) data tabulations that are to be submitted as part of
a product application to a regulatory authority such as the FDA
• The Submission Data Standards team of CLINICAL DATA INTER CHANGE
STANDARDS CONSORTIUM(CDISC) defines SDTM
• SDTM is built around the concept of observations collected about subjects who
participated in a clinical study
• Each observation can be described by a series of variables, corresponding to a
row in a dataset or table
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3. 1. Identifier variables, which identify the study, subject of the observation,
the domain, and the sequence number of the record
2. Topic variables, which specify the focus of the observation (such as
the name of a lab test)
3. Timing variables, which describe the timing of the observation (such as
start date and end date)
4. 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)
5. A fifth type of variable role, Rule, can express an algorithm or
executable method to define start, end, or looping conditions in the
Trial Design model
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4. QUALIFIER VARIABLES CAN BE FURTHER CATEGORIZED INTO
FIVE SUB-CLASSES:
1. Grouping Qualifiers are used to group together a collection of
observations within the same domain
2. 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
3. Synonym Qualifiers specify an alternative name for a particular
variable in an observation
4. Record Qualifiers define additional attributes of the observation
record as a whole
5. 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
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5. SUBMISSION METADATA MODEL USES SEVEN DISTINCT
METADATA ATTRIBUTES
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)
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6. Continue……
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
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7. GENERAL DOMAIN CLASSES:
• Most observations collected during the study should be divided
among three general observation classes: Interventions,
Events, or Findings
1. The Interventions class captures investigational treatments,
therapeutic treatments, and surgical procedures that are
intentionally administered to the subject
a. Concomitant Medications - CM
b. Exposure - EX
c. Substance Use - SU
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8. Continue……
2. The Events class captures occurrences or incidents
independent of planned study evaluations occurring during the
trial or prior to the trial
(e.g., 'adverse events' or 'disposition')
a. Adverse Events - AE
b. Disposition - DS
c. Medical History - MH
d. Protocol Deviations - DV
e. Clinical Events - CE
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9. Continue……
3. The Findings class captures the observations resulting from
planned evaluations to address specific questions such as
observations made during a physical examination, laboratory
tests, ECG testing, and sets of individual questions listed on
questionnaires.
a. ECG Tests - EG
b. Inclusion/Exclusion Exceptions - IE
c. Questionnaires - QS
d. Physical Examinations - PE
e. Pharmacokinetics Concentrations - PC
f. Subject Characteristics - SC
g. Vital Signs – VS etc.....
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10. REFERENCES
1. CDISC Study Data Tabulation Model / Submission Data Domain Models, V
2. CDISC Study Data Tabulation Model SDTM Implementation Guide V3.1.1
3. From Wikipedia, the free encyclopedia
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