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Tabulation dataset generation

Specifies the SDTM class for the domain

Tabulation dataset generation

Specifies the 2-letter domain code

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Implementation Options 

The general structure of tabulation datasets uses a normalized structure. In a normalized structure data are collected using a general variable for the name of the test and 1 variable for the collected results, resulting in a vertical data structure in which there is 1 record for each test/result. This is denote using N/A in this CDASH column. When a deformalized structures are used, data are collected using a unique variable name for each test, resulting in a wide, horizontal dataset with multiple test results in each record. This is denoted using Horizontal-Generic.

These seem to be options for variable names, One option is for variable names in a structure that matches the SDTM structure, while the other is a variable name within a denormalized structure.

Horizontal (Denormalized) and Vertical Data Structures (Normalized) 

1. In the CDASHIG metadata tables, many of the CDASH Findings class domains are presented in a normalized structure (1 record for each test) that is similar to the SDTM, even though many data management systems hold the data in a denormalized structure (1 variable for each test). When implementing CDASH in a denormalized structure, create variable names for the Findings --TEST and/or --TESTCD values. To do this, define the denormalized variable names using available CDISC Controlled Terminology for --TESTCD. Alternatively, CDASH variable names for data management systems allowing more than 8-character variable names can use CDASH variables using the following naming convention: <--TESTCD>_<-- SDTM variable name> where --TESTCD is the appropriate CT for the test code (e.g., DIABP_VSORRES, DIABP_VSLOC). Other naming convention may be used. See the CRFs in Section 8.3.18, Vital Signs, for examples of CDASH variable naming conventions.   

Tabulation dataset generation

Provides the data collection variable name

When the value in the Observation Class column is Findings

Provides the draft definition for the data collection variable to support understanding and intended usage. Values in this column are further described in Section 1.5, Known Issues. 

CRF and system design

Provides full sentence/question forms to prompt for data. Values in this column are: 

• Without text in brackets or parentheses, to be used as-is
• With text in brackets, indicating an option on the verb tense of the question or text that can be replaced with protocol-specified verbiage or controlled terminology
• With text in parentheses, indicating options for text (e.g., singular/plural) or optional text that may be omitted
• With text separated with a forward slash, indicating options for wording from which an implementer may choose.

When the value in the Observation Class column is Findings, controlled terms must be used to create question text for the data collection field on the CRF.

The CDASH Model can be referenced to create conformant alternative question text.

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Prompt 

CRF and system design

Tabulation dataset generation

Specifies the data type of the variable. Values for in this column are:

• Num for numeric data
• Char for character or alphanumeric date

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Collection Core 

CRF and system design

Specifies expectations for inclusion of the collection field on the CRF. Values are used with values in the Data Collection Scenario column to determine when a field will be present on the CRF. Values in this column are:

• HR, for fields that are Highly Recommended and should always be included on the CRF
• R/C, for fields that are Recommended/Conditional and should be included on the CRF per certain conditions
• O, for fields that are Optional and should be included on the CRF when needed to support data collection

When the value in this column is R/C, conditions are described in the Implementation Notes column.

Provides recommended example instructions for the data collection field to support authoring of study-level instructions for how to enter collected information on the CRF

Tabulation dataset generation

This column provides the suggested mapping to the tabulation dataset variable. Values in this column are:

• The name of the tabulation dataset variable to which the value of the data collection variable will be mapped
• N/A for when no direct mapping to a tabulation dataset variable is available
• SUPP–.QNAM

  Jira
  showSummary false server Issue Tracker (JIRA) serverId 85506ce4-3cb3-3d91-85ee-f633aaaf4a45 key TOBA-407

  for when the value of the data collection variable will be mapped as a supplemental qualifier

Tabulation dataset generation

CRF and system design

Specifies the name or names of the CDISC Controlled Terminology codelist or codelists associated with the data collection field

• The codelist name in parentheses, when 1 codelist is associated
• Codelist names in parentheses separated by commas, when more than 1 codelist is associated
• N/A for when controlled terminology is not associated with the data collection field and variable

Specifies CDISC CT terms or the name of the CDASH subset codelist associated with the data collection field and variable.Values in this column are:

• A controlled terminology term or a subset of terms from the codelist referenced in the Controlled Terminology Codelist Name column
• The name of the CDASH subset codelist in parentheses
• N/A for when specific controlled terms or CDASH subset terminology are not applicable

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Implementation Notes 

CRF and system design

Tabulation dataset generation

Collection field implementation

Provides further context for implementation of data collection fields and variables. Values in this column may include:

• Rationale for implementation guidance
• Additional instructions for implementing fields and variables on the CRF and in the underlying database structure
• Example values which could appear in the in the data collection field and variable
• Additional guidance for mapping values from data collection to tabulation variables

Example values provided are intended to support understanding and are not intended to influence decisions regarding what data to collect. For guidance on the selection of data to collect, please refer to the appropriate regulatory authority.