Understanding ADAPT in the SDTM TS Domain: Adaptive vs Non-Adaptive Trials

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Understanding ADAPT in the SDTM TS Domain: Adaptive vs Non-Adaptive Trials

Understanding ADAPT in the SDTM TS Domain: Adaptive vs Non-Adaptive Trials

The Study Data Tabulation Model (SDTM) plays a critical role in organizing and submitting clinical trial data. One of the parameters that regulatory agencies look for in the Trial Summary (TS) domain is the ADAPT parameter (TSPARMCD=ADAPT), which indicates whether the trial follows an adaptive design. In this blog post, we will explore the meaning of ADAPT and provide examples of adaptive and non-adaptive trials.

What is ADAPT in the TS Domain?

The ADAPT parameter identifies whether the clinical trial is adaptive (ADAPT=Y) or non-adaptive (ADAPT=N). An adaptive trial allows for modifications to the study design based on interim results, making the trial more flexible and often more efficient.

"Adaptive clinical trials allow for changes in design or hypotheses during the study based on accumulating data, without undermining the validity or integrity of the trial."

Example 1: Non-Adaptive Trial (ADAPT = N)

A non-adaptive trial follows a fixed protocol and does not allow for any changes during the study. Most traditional randomized controlled trials (RCTs) fall into this category. For example, a phase III trial that tests a drug against a placebo in a predefined number of patients without any modifications would be classified as non-adaptive.

STUDYID TSPARMCD TSVAL
ABC123 ADAPT N

In this case, the study ABC123 is a non-adaptive trial with no pre-planned modifications allowed during the course of the trial.

Example 2: Adaptive Trial (ADAPT = Y)

An adaptive trial allows changes to be made during the study based on interim analyses. These changes might include modifying sample size, adjusting dosing regimens, or even dropping treatment arms. Adaptive trials are common in oncology and rare disease studies, where efficient trial design is crucial due to limited patient populations.

For example, a phase II oncology trial might allow for dose adjustments or early termination based on early data. In this case, the trial would be classified as adaptive.

STUDYID TSPARMCD TSVAL
DEF456 ADAPT Y

The study DEF456 is an adaptive trial where the protocol allows for changes based on interim analysis.

Key Considerations for Adaptive Trials

When implementing an adaptive trial, it's essential to plan for certain regulatory and statistical considerations:

  • Pre-Specified Rules: Adaptations must be pre-specified in the protocol and reviewed by regulatory bodies.
  • Interim Analyses: Interim analyses require statistical rigor to avoid bias or misleading results.
  • Regulatory Approval: Regulatory agencies such as the FDA and EMA provide specific guidelines for adaptive trials, which must be strictly followed.

When is TSVAL set to "Y" for TSPARMCD=ADAPT?

The TSVAL variable is set to "Y" (Yes) for TSPARMCD=ADAPT if the study incorporates an adaptive design. An adaptive design allows for certain changes during the trial without compromising its validity. Examples of common adaptive designs include:

  • Sample Size Re-estimation: Adjusting the sample size based on interim data to ensure adequate power.
  • Early Stopping for Efficacy or Futility: Halting the trial early based on strong interim results or low likelihood of success.
  • Dose Adjustment: Changing dose levels according to participant responses.
  • Group Sequential Design: Using planned interim analyses to decide if the trial should continue or be modified.

If any of these design aspects apply, TSVAL for TSPARMCD=ADAPT would be "Y". Otherwise, it would be set to "N" for non-adaptive, fixed designs.

Example TS Domain Table

Here’s an example representation in the TS domain:

TSPARMCD TSPARM TSVAL Description
ADAPT Adaptive Design Y Indicates that the study has an adaptive design approach.

In regulatory submissions, such as to the FDA or PMDA, defining adaptive design parameters helps reviewers understand study flexibility and methods for ensuring trial integrity.

Conclusion

Understanding whether a trial is adaptive or non-adaptive is crucial for interpreting clinical trial data. Adaptive trials offer flexibility and efficiency but come with additional regulatory and statistical challenges. The ADAPT parameter in the TS domain provides a quick way to identify whether a trial has an adaptive design, allowing for more informed data review and analysis.

References

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