The Power of RETAIN Statement in SAS Programming: Advantages and Use Cases
The Power of RETAIN Statement in SAS Programming: Advantages and Use Cases
Author: Sarath
Date: October 10, 2024
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Category: SAS
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**StudySAS Blog: Mastering Clinical Data Management with SAS** 2024-09-24 12:45:00
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.
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
- CDISC SDTM Documentation – Official documentation for SDTM standards by CDISC.
- FDA Guidance on Adaptive Design – Guidance from the U.S. Food and Drug Administration on adaptive trials.
- EMA Guidelines on Clinical Trial Design – European Medicines Agency resources on adaptive and non-adaptive trial designs.
- SDTM Implementation Guide (SDTM IG) – Official SDTM Implementation Guide from CDISC.
**StudySAS Blog: Mastering Clinical Data Management with SAS** 2024-09-24 12:40:00
Understanding SDTM Trial Summary Domain: ACTSUB vs Screen Failures
In the world of clinical data management, the Study Data Tabulation Model (SDTM) plays a vital role in organizing and submitting clinical trial data to regulatory agencies. One of the most essential domains in SDTM is the Trial Summary (TS) domain, which provides key information about the clinical trial itself.
In this blog post, we will explore the Actual Number of Subjects (ACTSUB) and how it differs from screen failures. We will also reference regulatory guidelines and SDTM Implementation Guides to ensure a deeper understanding.
What is the TS Domain?
The Trial Summary (TS) domain contains high-level information about the clinical trial. This includes essential data such as the number of subjects, the start and end dates of the trial, trial objectives, and much more. The TSPARMCD variable defines various parameters such as the number of subjects or study arms in the trial.
What is TSPARMCD=ACTSUB?
ACTSUB stands for the “Actual Number of Subjects” in a clinical trial. This variable represents the number of participants who actually started the treatment or intervention after passing the screening phase.
“The actual number of subjects refers to the total number of participants who were enrolled in the study and received at least one treatment or underwent a key study procedure.”
This means that screen failures—subjects who were screened but did not qualify to proceed—are typically excluded from this count. Regulatory agencies such as the FDA and EMA expect only those subjects who participated in the study to be counted under ACTSUB.
How Are Screen Failures Captured in the TS Domain?
Screen failures are accounted for separately from ACTSUB in most cases. For instance, the TS domain may contain a different variable like TSPARMCD=SCRSUB, which captures the number of subjects who were screened. This would include those who did not pass the screening process.
Example Scenario: ACTSUB and Screen Failures
Let’s consider a hypothetical trial with 200 subjects:
- 250 subjects were screened.
- 50 of those subjects were screen failures (they did not meet eligibility criteria).
- The remaining 200 subjects were enrolled in the trial and participated in the treatment.
In this scenario, TSPARMCD=ACTSUB would be recorded as 200, while TSPARMCD=SCRSUB would be recorded as 250 to include all screened subjects, both successful and failures.
References and Guidelines
To further explore this topic, you can review the following references:
- CDISC SDTM Documentation – Official SDTM documentation by CDISC.
- FDA Clinical Trial Data Requirements – The U.S. Food and Drug Administration’s guidelines on submitting clinical trial data.
- EMA Clinical Trial Data Submission Guidelines – European Medicines Agency’s resources on clinical trial data.
- SDTM Implementation Guide (SDTM IG) – Implementation guidelines from CDISC for using SDTM in clinical trials.
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