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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.  The combined set of values for STUDYID, USUBJID, 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 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 domainalong 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 to identify individual qualifier values (SUPP-- records) related to multiple general observation class domain records that could be grouped, such as relating an of representing relationships when relating an NSV or attribution to a group of laboratory measurementsobservations in the parent dataset. Variable QORIG represents where values represented in IDVARVAL originated (e.g., 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 . 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.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

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,

Jira
showSummaryfalse
serverIssue Tracker (JIRA)
serverId85506ce4-3cb3-3d91-85ee-f633aaaf4a45
keyTOBA-801
applicants should put data from the primary evaluation into the parent dataset and data from the secondary evaluation into the Supplemental Qualifier datasets (SUPP--). Within each SUPP-- record, the value for QNAM should be formed by appending a "1" to the corresponding standard domain variable name. In cases where the standard domain variable name is already 8 characters in length, applicants should replace the last character with a "1" (incremented for each additional attribution).

Pagenav
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.