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The following table illustrates minimum conformance to standards for tabulation datasets.
| Metadataspec |
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| Num | Conformance | Implementation |
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| 1 | Representing all collected, assigned, and relevant derived data in applicable datasets | All data generated per scientific and regulatory requirements are included in tabulation datasets. | | 2 | Using domain specifications in this guide wherever applicable | A dataset is created using a domain specification in this guide when the scientific nature or role of the data is within the scope of a domain. Domains are extended or custom domain specifications are only used when data are different in nature and are not in scope for domains in this guide. | | 3 | DM is created| Jira |
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| showSummary | false |
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| server | Issue Tracker (JIRA) |
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| serverId | 85506ce4-3cb3-3d91-85ee-f633aaaf4a45 |
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| key | TOBA-378 |
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| For studies that assess the impact of tobacco products on the health of individuals, DM is required even if no other datasets are generated. | 4 | Following conventions for dataset naming | The dataset name is standardized per naming conventions and per controlled terminology where applicable. | | 54 | Following guidance for dataset record structure | Dataset content is aligned with the record structure specified per the domain specification. | | 65 | Following core designations | All Required and Expected tabulation| Jira |
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| showSummary | false |
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| server | Issue Tracker (JIRA) |
|---|
| serverId | 85506ce4-3cb3-3d91-85ee-f633aaaf4a45 |
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| key | TOBA-791 |
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variables are included as columns in the dataset. Required tabulation variables are populated for all records in the dataset. Permissible variables used to collect data are included in the dataset, even when no data for those variables were collected. | | 76 | Following conventions for variable naming | The names of variables in the dataset are standardized per domain specifications and other applicable guidance. Controlled terminology for domain prefixes is used as specified for variable naming. | | 87 | Following conventions for variable labelsThe labels for variables in the dataset are standardized per domain specifications and other applicable guidance . | 9 | Following guidance for variable types | The variables in the dataset are standardized for either numeric or character values as specified per the domain specification. | | 108 | Populating variable values in alignment with this guide | All variables in the dataset are populated as expected per this guide including per general and domain-specific guidance, controlled terminology, and formatting. |
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| Metadataspec |
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| Num | Conformance | Implementation |
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| 1 | ADaM fundamental principles are followed | - Datasets and associated metadata clearly and unambiguously communicate the content and source of the datasets supporting the statistical analyses performed in a clinical study.
- Datasets and associated metadata provide traceability to show the source or derivation of a value or a variable (i.e., the data's lineage or relationship between a value and its predecessor(s)). The metadata identify when and how analysis data have been derived or imputed.
- Datasets are readily usable with commonly available software tools.
- Datasets are associated with metadata to facilitate clear and unambiguous communication. Ideally the metadata are machine-readable.
- Datasets have a structure and content that allow statistical analyses to be performed with minimal programming. Such datasets are described as "analysis-ready." Datasets contain the data needed for the review and re-creation of specific statistical analyses. It is not necessary to collate data into analysis-ready datasets solely to support data listings or other nonanalytical displays.
| | 2 | The Subject-level Analysis Dataset (ADSL) is created | For studies that assess the impact of tobacco products on the health of individuals, ADSL and its related metadata are required even if no other analysis datasets are generated. | 3 | ADaM datasets follow the normative data found in the TIG | Datasets follow the fundamental principles defined in ADaM and adhere as closely as possible to TIG variable naming and other conventions. | | 43 | Traceability principles are followed | In ADaM, it is assumed that the original data sources for ADaM datasets are SDTM datasets, even when ADaM datasets are derived from other ADaM datasets. ADaM has features that enable traceability from analysis results to ADaM datasets and from ADaM datasets to SDTM datasets. These conventions must be followed for ADaM datasets with a CLASS value of BASIC DATA STRUCTURE, OCCURRENCE DATA STRUCTURE, and SUBJECT LEVEL ANALYSIS DATASET. Other analysis datasets should follow this convention where practical and feasible. |
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