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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

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. 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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  • 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. 
    • TOBA-393
  • All variables must use labels defined in Section 2.9.5, Predefined Standard Variables for ADSL, Predefined Standard Variables for BDS, and Section 2.9.7, Predefined Standard Variables for OCCDS (besides for the 2 exceptions described therein).
    • Metadataspec
    Issue Tracker (JIRA)(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
  • All other ADaM datasets (besides for ADSL) should be named AD + applicant-defined name (ADXXXXXX).
    • NumGuidance ForImplementation
    1Dataset 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
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    2Dataset naming
    • Analysis dataset naming has no predefined values. The only pre-defined name for analysis  datasets is ADSL which is suggested for
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        studies where a one-record-per-subject dataset is created to capture subject-level demographics, product usage, and/or trial experience information.jira
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    Jira
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    The exception to this general naming convention is the addition of the RF prefix for reference data that has been introduced in the TIG.jira
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    3Variable order
    • There is no variable ordering
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      defined 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.  
    4Variable 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. 
    5Variable labels
    • Descriptive labels per this guide, up to 40 characters, will be provided as data variable labels for all variables.
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    6Variable 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 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:
      • PARAMCD values will never be longer than 8 characters, so the length of that variable can be set to 8.
      • The length for variables that use controlled terminology can be set to the length of the longest term.
    7Variable 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 usage must be followed).

    8Missing 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. 

    9Splitting 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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