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When to Use CDISC Standards in Early Phase Clinical Trials

When to Use CDISC Standards in Early Phase Clinical Trials

Adopting CDISC standards in the earliest stages of clinical development helps maximize the impact of study data

Adoption of data standards in clinical trials is not only required for regulatory submissions but also useful for maximizing the impact of clinical studies. The Clinical Data Interchange Standards Consortium (CDISC) is a global not-for-profit organization that creates and communicates standards supporting the acquisition, exchange, submission, and archive of data for biopharmaceutical product development. Developed in collaboration with global experts, the CDISC standards seek to make clinical data easier to understand and interpret, even for those who are not directly involved in the clinical study.

While CDISC standards can be applied to all phases of clinical development, compliance with the standards is only mandatory for submissions to the U.S. Food and Drug Administration (FDA) and other regulatory agencies that require it. In this article, we discuss CDISC standards, explore their benefits and provide a framework for determining when to apply them in early-phase clinical studies.


Background on CDISC Standards for Clinical Studies

The main CDISC standards for clinical studies are Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM). With limited exceptions, regulatory submissions to the U.S. Food and Drug Administration (FDA) for studies started after December 17, 2016, require data to be submitted in CDISC format. Any data collected during the study, including both Electronic Case Report Form (eCRF) and external data, must be converted using CDISC standards and provided in SDTM datasets.

These datasets are used as the source data for ADaM datasets which are, in turn, the source data for analyses documented in the Clinical Study Report (CSR), Integrated Summaries of Safety (ISS), and Integrated Summaries of Efficacy (ISE). The FDA requires that these datasets and their corresponding Define-XMLs are submitted with reviewer’s guides that summarize the study protocol, statistical analysis plan, and metadata captured in the Define-XMLs. Ultimately, the objective of this data mapping process is to provide transparency and traceability of the analysis that was performed in a standard format.

CDISC standardsEarly integration of CDISC standards presents several benefits.

Deciding Whether to Adopt CDISC Standards in Early Phase Clinical Trials

Sponsors commonly wonder whether CDISC standards should be implemented from the beginning of clinical development, or whether it is more practical to hold off on conversion until it is known that the drug will be submitted for approval. Adoption of CDISC standards in early phase studies requires additional planning but leads to numerous downstream benefits.

Benefits of including CDISC standards in early phase clinical studies

Potential advantages of being CDISC compliant early in clinical development include:

  • Interoperability of clinical research databases. Designing a study based on CDISC standards ensures that data are recorded in the same way in each software system, allowing for seamless integration of all clinical trial information.
  • Data quality. If compliance checks are run on legacy data, it may not be possible to resolve any data issues that arise. Instead, running compliance checks proactively throughout a study allows for the early identification of potential data issues across SDTM datasets. These issues can then be queried and resolved by sites prior to database lock.
  • Time. When study data is compliant with CDISC standards, the team that will be performing the analysis will know how to set up SDTM and ADaM to support analysis needs, saving time.
  • Cost. If the analysis was originally completed using non-CDISC standards, an extra step will need to be performed after CDISC conversion to ensure that all new analysis using this new source of data matches the original analysis. Discrepancies are often found during this process of matching to previous results, requiring time-consuming troubleshooting which may surface errors in the original data.
  • Future use. Archiving data according to CDISC standards allows other researchers to extract data from existing datasets and perform new analyses on them, increasing the amount of useful clinical data that can be derived from a single study.


A framework for decision-making on CDISC inclusion

When weighing whether to use CDISC standards, sponsors should consider what their clinical trial objectives are, how study data will be used, and which resources are available to support implementation. CDISC standards are complex, and implementation may require training or external support. In certain circumstances, it may make sense not to adopt CDISC standards, for instance:

  • Budget priorities. For investigator-initiated or first-in-human trials, sponsors may prioritize minimizing cost and maximizing speed.
  • Exit plans. Sponsors planning an early exit from their pipeline asset may prefer to leave CDISC conversion to the acquirer who will take the drug through the later stages of development.
  • Data considerations. In some cases, sponsors may opt not to convert all data to CDISC. For instance, if protocol deviations are collected in Excel after database lock, the sponsor might decide to use the Excel file as the source rather than SDTM.

If a sponsor opts to forego CDISC in early phase trials but needs those data for submission to the FDA, the data will require conversion to CDISC format. The cost of conversion at a later stage of clinical development is often higher than implementing CDISC in early phase studies due to:

  • Learning curve. It takes time for a new team to get up to speed and understand how data was collected in a study in which they were not personally involved. Moreover, the database structure of legacy data will need to be retrofitted to CDISC standards, which may require complex mapping.
  • Troubleshooting. If discrepancies are identified in the process of matching previous results, troubleshooting may be required. If errors are discovered in the original data, it will be necessary to understand and communicate the impact of the error.
  • Unresolved data issues. If data issues cannot be resolved, it may be necessary to create programming workarounds and additional documentation describing the issue and how it was resolved.

Key Takeaway

Adoption of data standards supports the collection of high-quality evidence during clinical development. While the use of CDISC standards is associated with a variety of benefits, implementation presents both a technical challenge and a cost. Thus, determining when and how to implement CDISC standards in a clinical development program can be challenging. At Precision for Medicine, we have extensive experience helping sponsors identify the optimal time and approach for adopting CDISC standards.

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