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The Supplemental Qualifiers (SUPP–SUPP--) special-purpose dataset model is used to represent both nonstandard variables ( NSVs ) and attributions with their relationship to records in a parent dataset. SUPP-- datasets are also used to capture attributions. An attribution is typically an interpretation or subjective classification of 1 or more observations by a specific evaluator (e.g., a diagnosis provided by a pathologist or veterinarian). It is possible that different attributions may be necessary in some cases; SUPP-- provides a mechanism for incorporating as many attributions as are necessary. A SUPP-- dataset can contain both objective data (where values are collected or derived algorithmically) and subjective data (attributions where values are assigned by a person or committee). For objective data, the value in QEVAL will be null. For subjective data, the value in QEVAL should reflect the role of the person assigning the value (e.g., "PATHOLOGIST", "VETERINARIAN").

• Nonstandard variables (NSVs) which cannot be represented using standard variables from the domain or the SDTM
• Attributions 

The Related Records (RELREC) special-purpose dataset is used to describe relationships between records for a subject and relationships between datasets. 

The Comments (CO) special-purpose domain is used to capture unstructured free-text comments. 

Once confirmed, determine whether adding a variable from the SDTM will fit the need. If adding a variable from the SDTM meets the need, then a Supplemental Qualifier dataset will not be implemented. Supplemental Qualifier datasets will only be implemented for domains when data are not in scope for a variable defined in the SDTM.

The Related Records (RELREC) special-purpose dataset is used to describe relationships between records for a subject and relationships between datasets. RELREC will not be used to represent relationships already represented in Supplemental Qualifier (SUPP–) datasets for nonstandard variables or the Comments (CO) dataset for unstructured free-text.

Relationships represented in RELREC are collected relationships. The RELREC dataset should be used to represent either:

• Explicit relationships, (e.g., concomitant medications taken as a result of an adverse event); or
• Information of a nature that necessitates using multiple datasets and that may need to be examined together for analysis or proper interpretation.  

will only be used with datasets based on the general-observation classes described in the SDTM (Events, Findings, Interventions) and Demographics (DM).

There will be only 1 SUPP-- dataset for a parent dataset. Relationships between records in the SUPP-- dataset and observations in the parent dataset Subject records or datasets expressing a relationship are specified using the key variables STUDYID, RDOMAIN (the domain code of the record or dataset observation in the relationship), USUBJID or POOLID (for nonclinical studies only), along with IDVAR (the name of the   variable from the parent dataset that identifies the related recordobservation(s)) . Each record in the RELREC special-purpose dataset contains keys that identify a record (or group of records) and an identifier for the relationship which is stored in the RELID variable. The value of RELID is chosen by the applicant and it is recommended that applicants use a standard system or naming convention for RELID (e.g., all letters, all numbers, capitalized).

Relationships Between Records for a Subject

Relationships between records for a subject are specified using the key variables STUDYID, RDOMAIN, and USUBJID, along with IDVAR and IDVARVAL (the value of the identifying variable). and IDVARVAL (the value of the identifying variable). IDVAR and IDVARVAL will be populated in all SUPP-- datasets with the exception of SUPPDM. IDVAR and IDVARVAL will not be populated in SUPPDM. When populated, IDVARVAL will contain the value of the variable described in IDVAR. Single records can be related by using a unique-record-identifier variable, such as --SPID or --SEQ, in IDVAR. Groups of records can be related by using grouping variables such as --GRPID in IDVAR.   Using --GRPID can be a more efficient method of representing relationships in RELREC when relating a single or group of records in one dataset an NSV or attribution to a group of records observations in another the parent dataset. Variable RELTYPE will not be used when relating records for a subject and is only used when representing relationships between datasets. The value of RELID will be identical for all related records within USUBJID.

Relationships Between Datasets

Relationships between datasets are represented using a single record for each related dataset that identifies the key(s) of the dataset that can be used to relate the respective records. Relationships between datasets are specified using the key variables STUDYID, RDOMAIN, and IDVAR only. The values of variables USUBJID and IDVARVAL will be null as relationships between specific subject records will not be identified. The variable RELTYPE will identify the type of relationship between the datasets  Variable QORIG represents where values represented in IDVARVAL originated (e.g., a one-to-one or parent-child relationship). The allowable values for RELYPE are ONE and MANY per CDISC Controlled Terminology. This information defines how a merge/join would be written, and what would be the result of the merge/join. The possible combinations are:

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value was collected via CRF). QORIG be populated with origin types for SDTM datasets described in the CDISC Define-XML standard.

Nonstandard VariablesThe SDTM does not allow the addition of new variables. Therefore, the supplemental qualifiers special-purpose dataset model is used to capture nonstandard variables and their association to parent records in general-observation class (Events, Findings, Interventions) datasets and Demographics (DM). Supplemental qualifiers are submitted via a separate SUPP-- dataset for each domain containing sponsor-defined variables (see Section 8.3, Supplemental Qualifiers - SUPP-- Datasets).

SUPP-- represents the metadata and data for each nonstandard variableNSV/value combination . As the name "supplemental qualifiers" suggests, this dataset is intended to capture additional qualifiers for an observation. Data that represent separate observations should be treated as separate observations, either in this domain or another domain. The supplemental qualifiers dataset is structured similarly to the RELREC dataset in that it uses the same set of keys to identify parent records. Each SUPP-- record also includes the name of the qualifier variable being added (QNAM), the label for the variable (QLABEL), the actual value for each instance or record (QVAL), the origin (QORIG) of the value (see Sections 3.2.2.1, Origin Metadata, and 3.2.3, Value-level Metadata), and the evaluator (QEVAL) to specify the role of the individual assigning the value (e.g., pathologist, veterinarian).and relates this to the observation or observations qualified by the NSV in the parent dataset. The name, label, and value of the NSV are represented in SUPP-- dataset variables QNAM, QLABEL, and QVAL, respectively. The origin of the NSV value will be represented in variable QORIG. When the value of an NSV is assigned, variable QEVAL will represent the role of the individual or individuals who assigned the value.

Attributions

SUPP-- datasets are also used to capture attributions. An attribution is typically an interpretation or subjective classification of 1 or more observations by a specific evaluator (e.g., a diagnosis provided by a pathologist or veterinarian). It is possible that different attributions may be necessary in some cases; SUPP-- provides a mechanism for incorporating as many attributions as are necessary. A SUPP-- dataset can contain both objective data (where values are collected or derived algorithmically) and subjective data (attributions where values are assigned by a person or committee). A variable name, label, and value for the attribution are represented in SUPP-- dataset variables QNAM, QLABEL, and QVAL, respectively. The origin for the attribution will be represented in variable QORIG. For objective data, the value in QEVAL will be null. For subjective data, the value in QEVAL should reflect the role of the person assigning the value (e.g., "PATHOLOGIST", "VETERINARIAN").

The values for STUDYID, USUBJID, and POOLID should be unique for every record. There should not be multiple records in a SUPP-- dataset for the same QNAM value, as it relates to IDVAR/IDVARVAL for a USUBJID in a domain.

Just as use of the optional grouping identifier variable, --GRPID, can be a more efficient method of representing relationships in RELREC, it can also be used in a SUPP-- dataset to identify individual qualifier values (SUPP-- records) related to multiple general observation class domain records that could be grouped, such as relating an attribution to a group of laboratory measurements.

SDTM

The SDTM does not allow the addition of new variables. Therefore, the Supplemental Qualifiers special-purpose dataset model is used to capture non-standard variables (NSVs) and their association to parent records in general-observation class datasets (Events, Findings, Interventions), Demographics (DM), and Subject Visits (SV). Supplemental qualifiers are represented as separate SUPP-- datasets for each dataset containing sponsor-defined variables (see Section 8.4.2, Submitting Supplemental Qualifiers in Separate Datasets).

SUPP-- represents the metadata and data for each NSV/value combination. As the name suggests, this dataset is intended to capture additional qualifiers for an observation. Data that represent separate observations should be treated as separate observations. The Supplemental Qualifiers dataset is structured similarly to the RELREC dataset, in that it uses the same set of keys to identify parent records. Each SUPP-- record also includes the name of the qualifier variable being added (QNAM), the label for the variable (QLABEL), the actual value for each instance or record (QVAL), the origin (QORIG) of the value (see Section 4.1.8, Origin Metadata), and the evaluator (QEVAL) to specify the role of the individual who assigned the value (e.g., "ADJUDICATION COMMITTEE", "SPONSOR"). Controlled terminology for certain expected values for QNAM and QLABEL is included in Appendix C1, Supplemental Qualifiers Name Codes.

SUPP-- datasets are also used to capture attributions. An attribution is typically an interpretation or subjective classification of 1 or more observations by a specific evaluator, such as a flag that indicates whether an observation was considered to be clinically significant. It is possible that different attributions may be necessary in some cases; SUPP-- provides a mechanism for incorporating as many attributions as are necessary. A SUPP-- dataset can contain both objective data (where values are collected or derived algorithmically) and subjective data (attributions where values are assigned by a person or committee). For objective data, the value in QEVAL will be null. For subjective data, the value in QEVAL should reflect the role of the person or institution assigning the value (e.g., "SPONSOR", "ADJUDICATION COMMITTEE").

The combined set of values for the first 6 columns (STUDYID…QNAM) should be unique for every record. That is, there should not be multiple records in a SUPP-- dataset for the same QNAM value, as it relates to IDVAR/IDVARVAL for a USUBJID in a domain. For example, if 2 individuals (e.g., the investigator and an independent adjudicator) provide a determination regarding whether an adverse event is treatment-emergent, then separate QNAM values should be used for each set of information (e.g., "AETRTEMI", "AETRTEMA"). This is necessary to ensure that reviewers can join/merge/transpose the information back with the records in the original domain without risk of losing information.

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For Interventions and Events general-observation class domains, all subjective data are assumed to be attributed to the investigator. For observations that have primary and secondary evaluations of specific qualifier variables,