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1. Is compliant with one of the ADaM defined structures and follows the ADaM fundamental principles, OR
2. Follows the ADaM fundamental principles defined in the ADaM Model document and adheres as closely as possible to the TIG analysis variable naming and other conventions.

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ADaM datasets must adhere to certain fundamental principles as described in the ADaM Model document:

• ADaM datasets and associated metadata must clearly and unambiguously communicate the content and source of the datasets supporting the statistical analyses performed in a clinical study.
• ADaM datasets and associated metadata must 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 must identify when and how analysis data have been derived or imputed.
• ADaM datasets must be readily usable with commonly available software tools.
• ADaM datasets must be associated with metadata to facilitate clear and unambiguous communication. Ideally the metadata are machine-readable.
• ADaM datasets should have a structure and content that allow statistical analyses to be performed with minimal programming. Such datasets are described as "analysis-ready."
• ADaM 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 non-analytical displays.

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To assist review, ADaM datasets and metadata must clearly communicate how the ADaM datasets were created. The verification of derivations in an ADaM dataset requires having at hand the input data used to create the ADaM dataset. A CDISC-conformant submission includes both SDTM and ADaM datasets; therefore, it follows that the relationship between SDTM and ADaM must be clear. This requirement highlights the importance of traceability between the analyzed data (ADaM) and its input data (SDTM).

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Traceability establishes across-dataset relationships as well as within-dataset relationships. For example, the metadata for supportive variables within the ADaM dataset facilitates the understanding of how (and perhaps why) derived records were created.

There are two 2 levels of traceability:

1. Metadata traceability facilitates the understanding of the relationship of the analysis variable to its source dataset(s) and variable(s) and is required for ADaM compliance. This traceability is established by describing (via metadata) the algorithm used or steps taken to derive or populate an analysis variable from its immediate predecessor. Metadata traceability is also used to establish the relationship between an analysis result and ADaM dataset(s).
2. Datapoint traceability points directly to the specific predecessor record(s) and should be implemented if practical and feasible. This level of traceability can be very helpful when trying to trace a complex data manipulation path. This traceability is established by providing clear links in the data (e.g., by use of a --SEQ variable) to the specific data values used as input for an analysis value. The BDS and OCCDS structures were designed to enable datapoint traceability back to predecessor data.

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