Using CDISC to Ensure Traceability for Regulatory Authority Submission

stethoscope in doctors officeRegulatory authorities are recommending, or even requiring, that clinical data be submitted using standards developed by CDISC. The Food & Drug Administration (FDA) expects to make CDISC standards for regulatory submission mandatory by 2016, which means time is winding down to ensure that data capture and analysis systems are CDISC compliant. CROS NT has been a CDISC Gold Member since 2011 and explains how CDISC ensures traceability for regulatory submissions (or impending data transparency legislation!).

According to SAS, “traceability in context of ADaM datasets means providing the method followed to derive an analysis endpoint from source SDTM data”. Standard datasets were designed to meet the need of regulatory authorities to have data from different Sponsors, CROs and partners in the same structure, and therefore ADaM datasets must adhere to the property of traceability, meaning the path that led to the creation of an analysis value must be defined.

cdisc traceability path

CDISC Standards for Data Traceability

In order to ensure traceability, it is necessary to understand the relationship between analysis results, analysis datasets and SDTM datasets. There are two types of traceability: data-point traceability and metadata traceability. ADaM datasets allow for the creation of variables or observations that are not directly used for the statistical analysis but support traceability. For example, re-allocation of data may happen for early termination visits in accordance with the Statistical Analysis Plan.

Metadata traceability includes documentation required to clearly describe information that already exists in the SDTM datasets together with algorithms and methods used to derive an analysis result.

These two traceability methods will ensure the following for regulatory authority submissions:

  • Original/Observed Information (SDTM)
  • Derived/imputed information
  • Methods and algorithms for all derivations
  • Supportive or required information for the statistical analysis

Analysis-ready datasets, according to CDISC ADaM Implementation Guide Version 1.0, means minimal programming is required and no derivations should be done during programming of the statistical analysis but all variables and observations should be included in the dataset.

To ensure traceability, statistical programmers must prepare a clear metadata and describe the contents of all datasets and statistical analysis using the naming, labelling and variable conventions.

While the ADaM model can present many challenges in terms of adhering to traceability, there are ways of ensuring it. Standards, macros and the process currently used by the team should archived into global libraries. New procedures and standards should be established, and the team should have adequate training.

CROS NT and CDISC Mapping

CROS NT is a CDISC Gold Member, meaning it has constant access to new data standards and new documentation regarding CDISC standards. CROS NT has helped many companies incorporate CDASH, SDTM and ADaM standards into their organizations and mapped legacy studies to create the necessary consistency in formats. Our statistical programming team has developed excellent macros to reduce time, costs and ensure consistency.

For more information, send a request to our expert team.

Using CDISC to Ensure Traceability for Regulatory Authority Submission

stethoscope in doctors officeRegulatory authorities are recommending, or even requiring, that clinical data be submitted using standards developed by CDISC. The Food & Drug Administration (FDA) expects to make CDISC standards for regulatory submission mandatory by 2016, which means time is winding down to ensure that data capture and analysis systems are CDISC compliant. CROS NT has been a CDISC Gold Member since 2011 and explains how CDISC ensures traceability for regulatory submissions (or impending data transparency legislation!).

According to SAS, “traceability in context of ADaM datasets means providing the method followed to derive an analysis endpoint from source SDTM data”. Standard datasets were designed to meet the need of regulatory authorities to have data from different Sponsors, CROs and partners in the same structure, and therefore ADaM datasets must adhere to the property of traceability, meaning the path that led to the creation of an analysis value must be defined.

cdisc traceability path

CDISC Standards for Data Traceability

In order to ensure traceability, it is necessary to understand the relationship between analysis results, analysis datasets and SDTM datasets. There are two types of traceability: data-point traceability and metadata traceability. ADaM datasets allow for the creation of variables or observations that are not directly used for the statistical analysis but support traceability. For example, re-allocation of data may happen for early termination visits in accordance with the Statistical Analysis Plan.

Metadata traceability includes documentation required to clearly describe information that already exists in the SDTM datasets together with algorithms and methods used to derive an analysis result.

These two traceability methods will ensure the following for regulatory authority submissions:

  • Original/Observed Information (SDTM)
  • Derived/imputed information
  • Methods and algorithms for all derivations
  • Supportive or required information for the statistical analysis

Analysis-ready datasets, according to CDISC ADaM Implementation Guide Version 1.0, means minimal programming is required and no derivations should be done during programming of the statistical analysis but all variables and observations should be included in the dataset.

To ensure traceability, statistical programmers must prepare a clear metadata and describe the contents of all datasets and statistical analysis using the naming, labelling and variable conventions.

While the ADaM model can present many challenges in terms of adhering to traceability, there are ways of ensuring it. Standards, macros and the process currently used by the team should archived into global libraries. New procedures and standards should be established, and the team should have adequate training.

CROS NT and CDISC Mapping

CROS NT is a CDISC Gold Member, meaning it has constant access to new data standards and new documentation regarding CDISC standards. CROS NT has helped many companies incorporate CDASH, SDTM and ADaM standards into their organizations and mapped legacy studies to create the necessary consistency in formats. Our statistical programming team has developed excellent macros to reduce time, costs and ensure consistency.

For more information, send a request to our expert team.

Using CDISC to Ensure Traceability for Regulatory Authority Submission

stethoscope in doctors officeRegulatory authorities are recommending, or even requiring, that clinical data be submitted using standards developed by CDISC. The Food & Drug Administration (FDA) expects to make CDISC standards for regulatory submission mandatory by 2016, which means time is winding down to ensure that data capture and analysis systems are CDISC compliant. CROS NT has been a CDISC Gold Member since 2011 and explains how CDISC ensures traceability for regulatory submissions (or impending data transparency legislation!).

According to SAS, “traceability in context of ADaM datasets means providing the method followed to derive an analysis endpoint from source SDTM data”. Standard datasets were designed to meet the need of regulatory authorities to have data from different Sponsors, CROs and partners in the same structure, and therefore ADaM datasets must adhere to the property of traceability, meaning the path that led to the creation of an analysis value must be defined.

cdisc traceability path

CDISC Standards for Data Traceability

In order to ensure traceability, it is necessary to understand the relationship between analysis results, analysis datasets and SDTM datasets. There are two types of traceability: data-point traceability and metadata traceability. ADaM datasets allow for the creation of variables or observations that are not directly used for the statistical analysis but support traceability. For example, re-allocation of data may happen for early termination visits in accordance with the Statistical Analysis Plan.

Metadata traceability includes documentation required to clearly describe information that already exists in the SDTM datasets together with algorithms and methods used to derive an analysis result.

These two traceability methods will ensure the following for regulatory authority submissions:

  • Original/Observed Information (SDTM)
  • Derived/imputed information
  • Methods and algorithms for all derivations
  • Supportive or required information for the statistical analysis

Analysis-ready datasets, according to CDISC ADaM Implementation Guide Version 1.0, means minimal programming is required and no derivations should be done during programming of the statistical analysis but all variables and observations should be included in the dataset.

To ensure traceability, statistical programmers must prepare a clear metadata and describe the contents of all datasets and statistical analysis using the naming, labelling and variable conventions.

While the ADaM model can present many challenges in terms of adhering to traceability, there are ways of ensuring it. Standards, macros and the process currently used by the team should archived into global libraries. New procedures and standards should be established, and the team should have adequate training.

CROS NT and CDISC Mapping

CROS NT is a CDISC Gold Member, meaning it has constant access to new data standards and new documentation regarding CDISC standards. CROS NT has helped many companies incorporate CDASH, SDTM and ADaM standards into their organizations and mapped legacy studies to create the necessary consistency in formats. Our statistical programming team has developed excellent macros to reduce time, costs and ensure consistency.

For more information, send a request to our expert team.

Using CDISC to Ensure Traceability for Regulatory Authority Submission

stethoscope in doctors officeRegulatory authorities are recommending, or even requiring, that clinical data be submitted using standards developed by CDISC. The Food & Drug Administration (FDA) expects to make CDISC standards for regulatory submission mandatory by 2016, which means time is winding down to ensure that data capture and analysis systems are CDISC compliant. CROS NT has been a CDISC Gold Member since 2011 and explains how CDISC ensures traceability for regulatory submissions (or impending data transparency legislation!).

According to SAS, “traceability in context of ADaM datasets means providing the method followed to derive an analysis endpoint from source SDTM data”. Standard datasets were designed to meet the need of regulatory authorities to have data from different Sponsors, CROs and partners in the same structure, and therefore ADaM datasets must adhere to the property of traceability, meaning the path that led to the creation of an analysis value must be defined.

cdisc traceability path

CDISC Standards for Data Traceability

In order to ensure traceability, it is necessary to understand the relationship between analysis results, analysis datasets and SDTM datasets. There are two types of traceability: data-point traceability and metadata traceability. ADaM datasets allow for the creation of variables or observations that are not directly used for the statistical analysis but support traceability. For example, re-allocation of data may happen for early termination visits in accordance with the Statistical Analysis Plan.

Metadata traceability includes documentation required to clearly describe information that already exists in the SDTM datasets together with algorithms and methods used to derive an analysis result.

These two traceability methods will ensure the following for regulatory authority submissions:

  • Original/Observed Information (SDTM)
  • Derived/imputed information
  • Methods and algorithms for all derivations
  • Supportive or required information for the statistical analysis

Analysis-ready datasets, according to CDISC ADaM Implementation Guide Version 1.0, means minimal programming is required and no derivations should be done during programming of the statistical analysis but all variables and observations should be included in the dataset.

To ensure traceability, statistical programmers must prepare a clear metadata and describe the contents of all datasets and statistical analysis using the naming, labelling and variable conventions.

While the ADaM model can present many challenges in terms of adhering to traceability, there are ways of ensuring it. Standards, macros and the process currently used by the team should archived into global libraries. New procedures and standards should be established, and the team should have adequate training.

CROS NT and CDISC Mapping

CROS NT is a CDISC Gold Member, meaning it has constant access to new data standards and new documentation regarding CDISC standards. CROS NT has helped many companies incorporate CDASH, SDTM and ADaM standards into their organizations and mapped legacy studies to create the necessary consistency in formats. Our statistical programming team has developed excellent macros to reduce time, costs and ensure consistency.

For more information, send a request to our expert team.