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Generate CDISC Relating
Groups of Records in Separate Domain Datasets
%relrec (data = source dataset,
rdomain = related domain,
studyid = study identifier,
usubjid
= unique subject id,
idvar = identification variable,
output = output dataset,
basecode = SAS/Base Code);
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Where |
Is Type... |
And represents... |
|
data | C
(200) | A two
level dataset name specification in the form of libname.dataname.
|
|
rdomain | C
(200) |
Related domain abbreviation to the
current dataset |
|
studyid | C
(200) |
Study identifier. This can be
specified as a name or a numeric number. |
|
usubjid | C
(200) |
Unique Subject Identifier of the SDS
domain record(s). |
|
idvar | C
(200 optional) |
Identification variable that
identifies the related records. If there are multiple variables
then they are to be separated by spaces. |
|
output | C
(200 optional) |
Output dataset name. This can
be either a two level or one level name. If it is one level, it
will be defaulted to be produced in the same location as specified in
the data parameter. If no value is specified, it will generate a
data set name RELREC in the default location. |
|
basecode | C
(200 optional) |
Saved location of SAS Base code
that is used to create the relational records dataset. | Details
The fixed structure of CDISC SDTM data models restricts what goes
into the standard data domain datasets. There are five distinct
types of relationships which SDTM defines which allows for relationships
to be made to other special-purpose datasets. The five types
include:
- A relationship between a group of
records for a given subject within the same domain.
- A relationship between
independent records (usually in separate domains) for a subject, such as a
concomitant medication taken to treat an adverse event.
- A dependent relationship between
two (or more) datasets where all the records of one (or more) dataset(s)
have parent or counterpart record(s) in another dataset (or datasets) in a
different general class, such as lesion measurements associated with the
identification (and classification) of a lesion.
- A dependent relationship between
data that cannot be represented by a standard variable and a parent record
(or records) within a domain.
- A dependent relationship between
a comment and a parent record (or records) in other domains, such as a
comment recorded with an adverse event.
The output dataset that is going
to be generated will have the following structure:
| Variable | Variable Label | Type | Description |
| STUDYID | Study Identifier | Char | Study Identifier of the SDS domain record(s).
|
| RDOMAIN | Related Domain
Abbreviation | Char* | Domain Abbreviation of the SDS domain record(s).
|
| USUBJID | Unique Subject Identifier
| Char | Unique Subject Identifier of the SDS domain
record(s). |
| IDVAR | Identifier Variable | Char** | Value of the identifier variable in the general
class dataset that identifies the related record(s). Examples include
--SEQ and --GRPID. |
| IDVARVAL | Identifier Variable Value | Char | Value of identifier variable described in IDVAR.
If --SEQ is the |
| RELTYPE | Relationship Type | Char** | Identifies the hierarchical level of the records
in the relationship. Values should be either ONE or MANY. However,
values are only necessary when identifying a relationship between
datasets (as described in Section 8.3). |
| RELID | Relationship
Identifier | Char | Unique value within USUBJID that identifies the relationship. All
records for the same USUBJID that have the same RELID are considered
’related/associated.’ RELID can be any value the sponsor chooses, and
is only meaningful within the RELREC dataset to identify the
related/associated Domain records. | The IDVAR and IDVARVAL
can be different to reflect the domain for grouped variables. For
example, the group variables for the CM domain main are: CMGRPID, CMTRT, CMDECOD,
CMDOSE, CMDOSU, CMSTDTC and CMENDTC. The basecode parameter will generate
SAS/BASE code that will perform the same task of transposing input data into
the RELREC dataset. In addition, it will generate optional code that
can be used to perform the reverse transposition from RELREC back into the
original data structure. This is useful if you have legacy code that
you wish to use with the RELREC data. This reverse transposition
code can be regenerate the data that can then be used with the original
legacy code. Example %relrec (data=mylib.ae,
rdomain=ae,
studyid=1232,
usubjid = patnum,
idvar
= identification variable,
output = output dataset
);
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