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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 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 are specified using the key variables STUDYID, RDOMAIN (the domain code of the observation in the relationship), USUBJID or The combined set of values for STUDYID, USUBJID, POOLID (for nonclinical studies only), IDVAR, IDVARVAL, and QNAM will be unique for every record. There will 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, along with IDVAR (the name of the variable from the parent dataset that identifies the related observation(s)) 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, 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 when relating an NSV or attribution to a group of laboratory measurements.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 observations in the parent dataset. Variable QORIG represents where values represented in IDVARVAL originated (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.

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 ECG measurements.

Supplemental qualifiers are submitted via a separate SUPP-- dataset for each domain containing sponsor-defined variables (see Section 8.3, Supplemental Qualifiers - SUPP-- Datasets).

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

Nonstandard Variables

SUPP-- represents the metadata and data for each NSV/value combination 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.Nonstandard Variables

Attributions

An attribution is typically an interpretation or subjective classification of 1 or more observations by a specific evaluator. 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.

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For Interventions and Events The 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 variable/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).

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).

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