The Supplemental Qualifiers (SUPP–) special purpose dataset model is used to represent both nonstandard variables (NSVs) and attributions with their relationship to records in a parent dataset.
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, 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.
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.
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).
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). 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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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).
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.
Overview
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