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Sample ADaM Datasets: What You Need to Know About ADaM Standards – Novinite.com

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The Clinical Data Interchange Standards Consortium, or CDISC, creates medical research standards for the pharmaceutical and healthcare industry, particularly for medical research such as clinical trials. And one of the most important CDISC standards is the Analytical Data Model (ADaM) for clinical trial submissions.

What is Adam?

CDISC has different standards for clinical submissions, including content and data exchange standards. Content standards include Clinical Data Acquisition Standards Harmonization (CDASH), Protocol Representation Model (PRM), Study Data Tabulation Model (SDTM), and ADaM, defining sets of data and variables allowed in clinical trial submissions.

SDTM is the original source of ADaM data. From there, the analysis datasets enable the scientific and statistical analysis of the results of the clinical study. Additionally, the ADaM specifies standards and principles to ensure a clear sequence from clinical data collection to analysis.

While SDTM is the standard for organizing data collected in animal and human clinical trials, ADaM involves creating data ready for analysis. Here you can find other essential things you need to know about ADaM standards.

Sample ADaM Datasets

SDTM datasets are categorized for easy visual analysis. This way, a reviewer can filter data sets across the entire clinical trial and detect patterns and abnormalities. On the other hand, ADaM datasets are grouped together for easy calculation to get specific results in Table, Lists and Figure (TLF). This process requires variables from SDTM sources and then rearranging them into a different grouping to get the result.

Below are some sample ADaM datasets from the original CDISC Pilot Study 01:

  • vital signs (“advs”)

  • subject level (‘adsl’)

  • laboratory chemistry data(‘adlbc’)

  • concomitant medications (“adcm”)

  • adverse event (“adae”)

  • pharmacokinetic parameters (“adpp”)

This ADaM dataset follows the version 2.0 standard, which contains the augmented and modified dataset of the original CDISC Pilot Study 01.

ADaM Implementation Guide

Since the Food and Drug Administration (FDA) now requires clinical trial data submissions to use CDISC standards, many companies are making their analytics datasets ADaM compliant. To do this, clinical trial submissions should follow the ADaM Implementation Guide, which specifies dataset structures, variables, and standard solutions to ADaM implementation issues.

The ADaM standard data structures in the implementation guide include the following datasets:

The ADSL dataset contains one record per subject and variables such as subject-level population indicators, demographic information, and important dates. ADSL and associated metadata is a CDISC requirement for clinical trial submission.

The BDS dataset has one or more records per analysis time point, analysis parameter, and subject. It carries a core set of variables representing the actual data analyzed. In addition, this data set must include the variables PARAM (Parameter) and AVAL (Analysis Value) and/or AVALC (Analysis Value (C)).

Common Mistakes in ADAM Implementation

ADaMIG can lead to confusion and misinterpretations, resulting in non-compliant data sets. This is why it is important to fully understand the implementation guide to avoid causing dataset issues and ensure CDISC compliance.

Additionally, below are common ADaM implementation errors and recommended solutions:

ADaM is not suitable for generating a list. The creation of lists implies the use of the corresponding SDTM domain. You should only create an ADaM dataset if the list comes from a single data source but requires another calculation based on the date of the analysis or processing period. This is so because FDA reviewers are not programmers. Therefore, they may find it uncomfortable to combine data sets and derive variables.

This can lead to long hours of rework if you don’t know what you are analyzing when developing ADaM dataset specifications. It could also lead to analysis violations as programmers find it easier to update table/figure (TF) programs instead of going back and updating dataset specifications and programs Adam. You need to browse each dataset to see the required data and analyze and create annotations to define the needed ADaM datasets.

Traceability instills confidence in clinical trial results, linking them to source data for transparency between clinical study analysis results and ADaM datasets and SDTM domains. With this, use data point traceability to quickly determine the previous record. Also, include metadata traceability so that the reviewer can easily understand the relationship between the clinical study analysis data and the source data.

Incorporating reference values ​​into ADaM (ADSL) subject-level analysis datasets is not always a good idea. Examiners may think that all analysis results of a figure or table must come from a single data set. If including reference values ​​is not a requirement other than summarizing reference characteristics, you can save time and effort by not doing so.

Conclusion

ADaM standards are valuable in clinical research. On the one hand, being ADaM compliant can speed up the review and approval process for a clinical trial. That said, companies conducting clinical studies should have the right information about the standards set out in the ADaM Implementation Guide for easier compliance.

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