Requirements for data submission are defined and managed by the regulatory authorities to whom data are submitted. This section describes general requirements for datasets that may be part of a submission. However However, additional conventions may be defined by regulatory bodies or negotiated with regulatory reviewers. In such cases, additional requirements must be followed.
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Observations about tobacco products and study subjects generated to support a submission are represented in a series of datasets aligned with logical groupings of data per domains.
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into domains. Domains described in this guide are generally aligned with implementation of a single dataset file in which to represent data in scope for a domain.All datasets are structured as flat files with rows representing observations and columns representing variables.In some cases, a dataset implemented for a domain may be split into physically separate dataset files to support submission when needed and as allowable by the regulatory authority.
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| 1 | Dataset content | Data represented in datasets will include the following per regulatory requirements, scientific needs, and standards in this guide: - Data as originally collected or received (using controlled terminology where applicable) to support the submission
- Data from external references relevant to the submission (e.g., study protocol)
- Data assigned per conventions in the TIG
- Data derived per regulatory and TIG conventions
| | 2 | Dataset naming | - Domain datasets based on the SDTM general observations classes will be named using the 2-character code for the domain or using the applicable 4-character code when a dataset is split (e.g., LB, LBHM).
- Supplemental Qualifier
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- datasets will be named using "SUPP" concatenated withthe 2-character domain code for the parent domain (e.g., SUPPDM, SUPPFA) or the
- 4-character code for the parent dataset when a dataset is split (e.g.
- , SUPPFACM).
- All other datasets will be named using the code for the domain or dataset (e.g., DM, RELREC).
| | 3 | Variable order | - Dataset variables will be ordered per guidance in the SDTM.
- Variable order in TIG domain specifications aligns with variable order in the SDTM.
| | 4 | Variable names | - Variables will be named per guidance in the SDTM. The SDTM guidance uses fragment names in the CDISC Non-Standard Variables Registry.
- Variable names in TIG domain specifications
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- align with naming conventions in the SDTM.
- Variable names will be 8 characters or less and uppercase.
| | 5 | Variable labels | Descriptive labels per this guide, up to 40 characters, will be provided as data variable labels for all variables, including Supplemental Qualifier variables. | | 6 | | When variable length is referenced in the TIG, this refers to the length in bytes of ASCII character strings. - The maximum length of character
- variables is 200 characters,
- and the full 200 characters should not be used unless necessary.
- Applicants will consider the nature of the data and apply reasonable, appropriate lengths to variables. For example:
- --TESTCD and IDVAR values will never be
- longer than 8 characters, so the
- lengths of those variables can
- be set to 8.
- The length for variables that use controlled terminology can be set to the length of the longest term.
| | 7 | Variable value text case | - Values from controlled terminology or response values for QRS instruments specified by the instrument documentation will be in the case specified by those sources.
- Otherwise, text data will be represented in upper case (e.g., NEGATIVE).
| | 8 | Missing variable values | Missing values for individual data items will be represented by nulls. | | 9 | | A domain dataset may be split into physically separate datasets to support submission when needed and as allowable by the regulatory authority. The following conventions must be adhered to when splitting domains into separate datasets: - A domain based on a General Observation Class may be split according to values in variable --CAT. When a domain is split on --CAT, --CAT must not be null.
- The Findings About Events or Interventions (FA) domain may be split according to values in variable the domain in which the interventions or events in --OBJ .
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key | TOBA-95are represented (or would be represented).
To ensure split datasets can be appended back into 1 domain dataset: - The value of DOMAIN must be consistent across the separate datasets as it would have been if they had not been split (e.g., LB, FA).
- All variables that require a domain prefix (e.g., --TESTCD, --LOC) must use the value of DOMAIN as the prefix value (e.g., LB, FA).
- --SEQ must be unique within USUBJID for all records across all the split datasets. If there are 1000 records for a USUBJID across the separate datasets, all 1000 records need unique values for --SEQ.
- When relationship datasets (e.g., SUPPxx, FAxx, CO, RELREC) relate back to split parent domains, the value of IDVAR will be from a variable with unique values for each observation. When possible, the variable represented in IDVAR will have values from collected data (e.g., SPID, RECID), but variables with derived values may also be used (e.g., --SEQ).
- Permissible variables included in one split dataset need not be included in all split datasets.
- For domains with 2-letter domain codes, split dataset names can be up to 4 characters in length. For example, if splitting by --CAT, dataset names would be the domain name plus up to 2 additional characters to indicate the value of --CAT (e.g., LBHM for LB if the value of --CAT is HEMATOLOGY). If splitting Findings About by parent domain, then the dataset name would be the domain code, "FA", plus the two-character domain code for parent domain code (e.g., "FACM"). The 4-character dataset-name limitation allows the use of a Supplemental Qualifier dataset associated with the split dataset.
- Supplemental Qualifier datasets for split domains will also be split. The nomenclature will include the additional 1 to 2 characters used to identify the split dataset (e.g., SUPPLBHM, SUPPFACM). The value of RDOMAIN in the SUPP-- datasets would be the 2-character domain code (e.g., LB, FA).
- In RELREC, if a dataset-level relationship is defined for a split Findings About domain, then RDOMAIN will contain the 4-character dataset name, rather than the domain name "FA" (e.g., the value of RDOMAIN will be FACM).
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Analysis Datasets
Observations about tobacco products and study subjects generated to support analysis in a submission are represented in a series of datasets based on the CLASS values described in the TIG. Datasets described in this guide are generally created to support a certain type of analysis, but sometimes analysis datasets are created to support the creation of a subsequent dataset that will be used for analysis. All datasets are structured as flat files with rows representing observations and columns representing variables.
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| Num | Guidance For | Implementation |
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| 1 | Dataset content | Data represented in datasets will include the following per regulatory requirements, scientific needs, and standards in this guide: - Data as originally collected or received (using controlled terminology where applicable) to support the submission
- Data from external references relevant to the submission (e.g., reference data)
- Data assigned per conventions in the TIG
- Data derived per regulatory and TIG conventions
| | 2 | Dataset naming | - Analysis dataset naming has no predefined values
- . The only pre-defined name for analysis datasets is ADSL which is suggested for studies where a one-record-per-subject dataset is created to capture subject-level demographics, product usage, and/or trial experience information.
- All other ADaM datasets (besides for ADSL)
- should be named AD + applicant-defined name (ADXXXXXX). The exception to this general naming convention is the addition of the RF prefix for reference data that has been introduced in the TIG.
- There is no rule that noncompliant datasets must start with AX or that they cannot start with AD.
- ADaM datasets should be named logically, if possible, and consistent naming conventions should be used across studies within a submission.
| | 3 | Variable order | ordering defined - variable ordering defined for the ADaM standards, although having variables ordered together within a variable group helps review and dataset understanding.
- Variable order in the ADaM dataset must match the order in the define.xml file.
| | 4 | Variable names | - Variables will be named per ADaM guidance, which uses fragment names in the CDISC NSV Registry.
- Variable names in TIG ADaM specifications align with naming conventions in ADaM.
- Variable names will be 8 characters or less and uppercase.
| | 5 | Variable labels | - Descriptive labels per this guide, up to 40 characters, will be provided as data variable labels for all variables.
- All variables must
| | 6 | Variable length | When variable length is referenced in the TIG, this refers to the length in bytes of ASCII character strings. - The maximum length of character variable variables is 200 characters, which and the full 200 characters should not be used unless necessary.
- Applicants will consider the nature of the data and apply reasonable, appropriate lengths to variables. For example:
- The length of flags will always be 1.
- PARAMCD PARAMCD values will never be more longer than 8 characters, so the length of that variable can always be set to 8.
- The length for variables that use controlled terminology can be set to the length of the longest term.
| | 7 | Variable value text case | Variable value text case generally depends on the variable usage and how it is presented on outputs (but there is no requirement that this | | 8 | Missing variable values | Missing values for individual data items will be represented by nulls if necessary for analysis. Otherwise, it is up to the dataset creator whether to include missing values in an analysis dataset. | | 9 | Splitting datasets | An analysis dataset may be split into physically separate datasets to support submission when needed. ADaM currently has no conventions as to the proper way to split analysis datasets, although like types of data should have similar dataset naming. |
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