CDSIC SDTM Standards (2)

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General Observation Classes (contd)

Events

    Captures planned protocol milestones such as randomization , study completion ,adverse reactions

Findings

  Evaluations/examinations to address specific questions (when in doubt it’s a finding)

Others

   Special purpose domains( like Demography , comments ),  trial design , relationship datasets 

Core Variables :

 

•         A required variable is any variable that is basic to the identification of a data record (i.e. cannot be null)

•         An expected variable is any variable necessary to make a record meaningful in the context of a specific domain (variable should be included); Some values may be null

•         Permissible variables should be used as appropriate when collected or derived.

Fundamentals of SDTM

 

The 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.

 Each variable can be classified according to its Role.

 A Role determines the type of information conveyed by the variable about each distinct observation and how it   can be used.

Variables can be classified into five major roles:

Identifier variables, such as those that identify the study, subject,

 domain, and 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 the 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, and branching or looping conditions in the Trial Design model

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, --TEST and --LOINC which are

         equivalent terms for a --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.

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:

1.      as the dataset name,
2.      the value of the DOMAIN variable in that dataset,
3.      as a prefix for most variable names in that dataset,
4.     as a value in the RDOMAIN variable in relationship tables

Submission Metadata Model uses seven distinct metadata attributes to be defined for each dataset variable in the metadata definition document:

•         The Variable Name (limited to 8-characters for compatibility with the SAS System 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 or source of each variable

•         The Role of the variable, which determines how the variable is used in the dataset. Roles are used to represent the categories of variables as Identifier, Topic, Timing, or the five types of Qualifiers. Since these roles are predefined for all domains that follow the general classes, they do not need to be specified by sponsors in their define data definition document.

•         Comments or other relevant information about the variable or its data

General Rules

•         The Identifier variables, STUDYID, USUBJID, DOMAIN, and --SEQ are required in all domains based on the general observation classes. Other Identifiers may be added as needed.

•          Any Timing variables are permissible for use in any submission dataset based on a general observation class except where restricted by specific domain assumptions.

•          Any additional Qualifier variables from the same general observation class may be added to a domain model except where restricted by specific domain assumptions.

•          The SDTM allows for the inclusion of the sponsors non-SDTM variables using the Supplemental Qualifiers .

•         Standard variables must not be renamed or modified

•         As long as no data was collected for Permissible variables, a sponsor is free to drop them