SDTM aCRF Annotation Checklist

-
SDTM aCRF Annotation Checklist

SDTM aCRF Annotation Checklist

By Sarath Annapareddy

Introduction

Creating an SDTM Annotated Case Report Form (aCRF) is a critical step in clinical trial data submission. It ensures that data collected in the CRF maps correctly to SDTM domains, adhering to regulatory and CDISC standards. This checklist serves as a guide to creating a high-quality SDTM aCRF ready for regulatory submission.

1. General Formatting

  • Ensure the aCRF uses the latest SDTM IG version relevant to the study.
  • The document should be clean, legible, and free of overlapping annotations.
  • Page numbers in the aCRF should align with the actual CRF pages.
  • Annotations must be in English, clear, and consistently formatted.
  • Use color coding to differentiate domain mappings, derived variables, and special-purpose annotations.

2. Domain-Level Annotations

  • Annotate each field on the CRF with the corresponding SDTM variable name (e.g., DMAGE, LBTEST).
  • Ensure every field includes an appropriate domain prefix (e.g., DM, AE).
  • Unmapped fields should be labeled with "Not Mapped" or "NM".
  • Ensure proper usage of variable cases (e.g., all uppercase for SDTM variable names).

3. Data Collection Fields

  • Map demographic fields (e.g., SEX, RACE) to the `DM` domain.
  • Adverse event fields (e.g., event name, severity) should map to the `AE` domain.
  • Laboratory test results and units should map to the `LB` domain.
  • Exposure data (e.g., drug start/stop dates) must align with the `EX` domain.
  • Use the `DV` domain for protocol deviations and the `MH` domain for medical history.

4. Special-Purpose Domains

  • SUPPQUAL should be used for non-standard variables.
  • RELREC annotations are required for defining relationships between domains.
  • Free-text comments should map to the `CO` domain.
  • Trial design fields should map to domains like `TA`, `TV`, and `TS`.

5. Derived and Computed Variables

  • Derived variables must be clearly labeled (e.g., "DERIVED" or "CALCULATED").
  • Ensure annotations for variables like BMI reference all contributing fields (e.g., height and weight).
  • Visit variables (e.g., VISITNUM, VISITDY) should align with RFSTDTC.

6. Date and Time Variables

  • All date fields must follow ISO 8601 format (e.g., AESTDTC, EXSTDTC).
  • Derived date variables like VISITDY should be calculated relative to RFSTDTC.

7. Validation and Quality Control

  • Validate the aCRF against the finalized SDTM datasets.
  • Ensure alignment with the Define.xml document.
  • Conduct reviews by the programming and data management teams.
  • Perform a completeness check to ensure no fields are left unannotated.

8. Regulatory Submission Readiness

  • Ensure compliance with the requirements of regulatory authorities (e.g., FDA, PMDA).
  • Submit the aCRF in a searchable, bookmarked PDF format.
  • Verify that all color-coded annotations are visible in grayscale for printed versions.
  • Include a cover page with the study title, protocol number, and version.

Conclusion

A well-annotated SDTM aCRF is crucial for successful regulatory submissions. By following this checklist, you can ensure your aCRF meets compliance requirements and demonstrates traceability between the CRF, datasets, and Define.xml. This meticulous process not only ensures regulatory approval but also enhances the credibility of your clinical trial data.

© 2024 Rupee Stories. All rights reserved.

Popular posts from this blog

SAS Interview Questions and Answers: CDISC, SDTM and ADAM etc

-
1) What do you know about CDISC and its standards? CDISC stands for Clinical Data Interchange Standards Consortium and it is developed keeping in mind to bring great deal of efficiency in the entire drug development process. CDISC brings efficiency to the entire drug development process by improving the data quality and speed-up the whole drug development process and to do that CDISC developed a series of standards, which include Operation data Model (ODM), Study data Tabulation Model (SDTM) and the Analysis Data Model ADaM ). 2) Why people these days are more talking about CDSIC and what advantages it brings to the Pharmaceutical Industry? A) Generally speaking, Only about 30% of programming time is used to generate statistical results with SAS®, and the rest of programming time is used to familiarize data structure, check data accuracy, and tabulate/list raw data and statistical results into certain formats. This non-statistical programming time will be significantly reduced...
Read more

Comparing Two Methods for Removing Formats and Informats in SAS: DATA Step vs. PROC DATASETS

-
Comparing Two Approaches to Removing Formats and Informats in SAS Comparing Two Approaches to Removing Formats and Informats in SAS When working with SAS datasets, there are times when you need to remove formats and informats that have been previously assigned to variables. Two primary approaches can be used for this task: Using the DATA Step Using the PROC DATASETS Procedure This article compares and contrasts these two approaches to help you determine which method is most appropriate for your needs. Approach 1: Using the DATA Step The DATA step is a versatile and commonly used method for removing formats and informats. By assigning variables to a null format or informat, you can effectively remove these attributes from your dataset. Example: data mydata_clean; set mydata; format _all_; informat _all_; run; In this example, the mydata dataset is processed in the DATA step, and...
Read more

Studyday calculation ( --DY Variable in SDTM)

-
USE OF THE “STUDY DAY” VARIABLES The permissible Study Day variables (--DY, --STDY, and --ENDY) describe the relative day of the observation starting with the reference date as Day 1. They are determined by comparing the date portion of the respective date/time variables (--DTC, --STDTC, and --ENDTC) to the date portion of the Subject Reference Start Date (RFSTDTC from the Demographics domain). The Subject Reference Start Date (RFSTDTC) is designated as Study Day 1. The Study Day value is incremented by 1 for each date following RFSTDTC. Dates prior to RFSTDTC are decremented by 1, with the date preceding RFSTDTC designated as Study Day -1 (there is no Study Day 0). This algorithm for determining Study Day is consistent with how people typically describe sequential days relative to a fixed reference point, but creates problems if used for mathematical calculations because it does not allow for a Day 0. As such, Study Day is not suited for use in subsequent numerical computations...
Read more