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SDTM Chit-sheet

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SDTM Chit-sheet Empty SDTM Chit-sheet

Post  pallav on Sat Mar 31, 2012 2:44 am


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
A collection of observations on a particular topic is considered a domain.
Each observation can be described by a series of named variables.

General Observation Classes -------

The majority of observations collected during a study can be divided among three general classes: Interventions, Events, or Findings:

1. The Interventions class captures
a. Investigational treatments
b. Therapeutic treatments
c. Surgical procedures that are intentionally administered to the subject either as specified by the study protocol (e.g., .exposure.), or preceding or coincident with the study assessment period (e.g., .concomitant medications.)

2. The Events class captures

a. Events are the unplanned clinical occurrences that the patient experiences over the course of trial.
b. Planned protocol milestones such as randomization and study completion (.disposition.)
c. Occurrences or incidents independent of planned study evaluations occurring during the trial (e.g., .adverse events.) and
d. Prior to the trial (e.g., .medical history.)

3. The Findings class captures

a. the observations resulting from planned evaluations to address specific questions such as observations made during a
a. physical examination,
b. laboratory tests,
c. histopathology,
d. ECG testing and questions listed on questionnaires.

Metadata -
Each dataset/domain is described by metadata definitions that provide information about the variables used in the dataset.

Define specifies seven distinct metadata attributes to describe SDTM data:
1. The Variable Name (limited to 8 characters for compatibility with the SAS 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 of each variable, such as whether it was collected on a CRF or derived
6. The Role of the variable, which determines how the variable is used in the dataset. For the V3.x domain models, Roles are used to represent the categories of variables such as Identifier, Topic, Timing, or the five types of Qualifiers. Since these roles are predefined for all domains that follow the general observation classes, they do not need to be specified by sponsors in their Define data definition document.
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.

Domain -----

Observations are reported in a series of domains.
A domain is defined as a collection of observations with a specific topic about a subject.
Typically, each domain is represented by a single dataset

1. Special-Purpose Domains :
a. Demographics — DM
b. Comments — CO
c. Subject Elements — SE
d. Subject Visits — SV

2. Interventions General Observation Class:
a. Concomitant Medications — CM
b. Exposure — EX
c. Substance Use — SU

3. Events General Observation Class:
a. Adverse Events — AE
b. Disposition — DS
c. Medical History — MH
d. Protocol Deviations — DV
e. Clinical Events — CE

4. Findings General Observation Class:
a. ECG Test Results — EG
b. Inclusion/Exclusion Criterion Not Met — IE
c. Laboratory Test Results — LB
d. Physical Examination — PE
e. Questionnaires — QS
f. Subject Characteristics — SC
g. Vital Signs — VS
h. Drug Accountability — DA
i. Microbiology Specimen — MB
j. Microbiology Susceptibility Test — MS
k. PK Concentrations — PC
l. PK Parameters — PP
m. Clinical Findings — CF

5. Trial Design Domains:
a. Trial Arms — TA
b. Trial Elements — TE
c. Trial Visits — TV
d. Trial Inclusion/Exclusion Criteria — TI
e. Trial Summary — TS

6. Special Purpose or Relationship Datasets: (Non-SDTM variables)

a. Supplemental Qualifiers — SUPPQUAL or multiple SUPP-- datasets
b. Related Records —RELREC

A set of safety domains based on the three general observation classes, typically including
EX (Exposure)
CM (Concomitant Medication)

Events ---
AE (Adverse Event)
DS (Disposition)
MH (Medical History)

Finding ---
IE (Inclusion/Exclusion criteria not met)
LB (Lab Test Results)
VS (Vital Signs)

SDTM variables can be classified into five major roles ----

1. Identifier variables (identify the study, subject, domain, sequence no of the record)

2. Topic variables (specify the focus of the obs -- name of a lab test)

3. Timing variables (describe the timing of an observation -- start date and end date)

4. Qualifier variables (include additional illustrative text, or numeric values that describe the results or additional traits of the obs -- 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)

i. Grouping Qualifiers are used to group together a collection of observations within the same domain (e.g., HEMATOLOGY as a category classification for laboratory results).
ii. Result Qualifiers describe the specific results associated with the topic variable for a finding (e.g., the original result and the standardized result).
iii. Synonym Qualifiers specify an alternative name for a particular variable in an observation (e.g., the verbatim term and the preferred term for an adverse event).
iv. Record Qualifiers define additional attributes of the observation record as a whole (e.g., the reason a medication was taken).
v. Variable Qualifiers are used to further modify or describe a specific variable within an observation (e.g., the units for a laboratory result).

5. Rule variables (express an algorithm or executable method to define start, end, or looping conditions in the Trial Design model)

The presence of an asterisk (*) in the 'Controlled Terms or Format' column indicates that a discrete set of values (controlled terminology) exists or is expected for this variable. This set of values may be sponsor defined in cases where standard vocabularies have not yet been identified.

The --SEQ variable was created so that a unique record could be identified consistently across all of these domains via its use, along with STUDYID, USUBJID, and DOMAIN.


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