Protocol Version Mapping in SDTM Disposition Events: A Comprehensive Guide

-

Key Concept: The decision to map protocol version information in SDTM Disposition (DS) domain requires careful analysis of its relationship to disposition events and understanding of data management requirements.

Understanding Protocol Version's Role in Disposition Events

Protocol versions can significantly impact disposition events in clinical trials. Their relationship to these events determines the appropriate mapping strategy within the SDTM structure. This relationship can be categorized into two main types:

Direct Impact Relationship

When protocol version changes directly cause or influence disposition events, such as:

  • Subject withdrawal due to protocol amendment modifications
  • Study discontinuation resulting from significant protocol changes
  • Protocol-mandated subject transfers between treatment arms

Contextual Relationship

When protocol version provides important context but doesn't directly cause the disposition event:

  • Standard discontinuations occurring under different protocol versions
  • Administrative changes documented in protocol updates
  • Background information for data analysis

Decision Framework for Protocol Version Mapping

To determine the appropriate mapping strategy, follow this structured evaluation process:

1. Impact Analysis

Evaluate the protocol version's influence on disposition events by examining:

  • Direct causality between protocol changes and subject disposition
  • Regulatory requirements for tracking protocol version information
  • Statistical analysis requirements for protocol version stratification

2. Data Structure Assessment

Consider the following aspects of your data structure:

  • Existing variable relationships in the DS domain
  • Need for protocol version traceability
  • Impact on data analysis and reporting requirements

Protocol Version Mapping Methods: Decision Guide

Key Principle: The choice of mapping method should be driven by the relationship between the protocol version and the disposition event, regulatory requirements, and analysis needs. Each method serves a specific purpose and has distinct advantages.

Method 1: DSREFID Mapping

Use DSREFID mapping when protocol version information is fundamental to understanding or explaining the disposition event. This method is most appropriate in the following scenarios:

When to Use DSREFID:
  • The disposition event occurs as a direct result of a protocol amendment or version change. For example, when a subject withdraws because new procedures were introduced in a protocol amendment.
  • Protocol version changes trigger mandatory subject discontinuation or transfer between treatment arms.
  • Regulatory requirements specifically mandate tracking the relationship between protocol versions and disposition events.
  • The protocol version is essential for understanding the context of the disposition decision and will be needed for primary analysis.

Method 2: Supplemental Qualifiers (SUPPDS)

Use supplemental qualifiers when protocol version information provides important context but isn't directly causal to the disposition event. This approach is best suited for:

When to Use SUPPDS:
  • Protocol version information needs to be preserved for traceability but isn't directly related to the disposition decision.
  • The information might be needed for secondary analyses or regulatory documentation.
  • You need to maintain protocol version history without implying direct causality with disposition events.
  • The protocol version is part of standard documentation requirements but doesn't affect the interpretation of the disposition event.

Method 3: Custom Variables

Consider creating custom variables (with sponsor and regulatory approval) in these situations:

When to Use Custom Variables:
  • Your study has unique protocol version tracking requirements that don't fit well in standard variables.
  • You need to capture multiple aspects of protocol versioning (e.g., both amendment number and version date).
  • Sponsor-specific requirements necessitate specialized protocol version tracking.
  • The relationship between protocol versions and disposition events needs to be analyzed in ways not supported by standard variables.

Method 4: Comments or DSTERM

Include protocol version information in comments or DSTERM when:

When to Use Comments/DSTERM:
  • The protocol version provides helpful background information but isn't required for analysis.
  • You need to provide additional context about protocol version impacts without formal tracking.
  • The information is purely descriptive and won't be used in analyses.
  • You need to capture protocol version details in a narrative format.

Decision Support Matrix

Characteristic DSREFID SUPPDS Custom Variable Comments/DSTERM
Direct Causality Required Optional Variable No
Analysis Impact Primary Secondary Study-specific None
Traceability High Medium High Low
Implementation Complexity Medium Low High Low

Mapping Implementation Strategies

Strategy 1: DSREFID Mapping

USUBJID DSSEQ DSTERM DSDECOD DSREFID DSSTDTC
123-45678 1 Protocol Amendment Withdrawal WITHDRAWAL PROT-V2.0-AMD3 2024-01-15

Strategy 2: Supplemental Qualifiers

STUDYID RDOMAIN USUBJID IDVAR IDVARVAL QNAM QVAL
STUDY001 DS 123-45678 DSSEQ 1 PROTVER 2.0

Best Practices and Recommendations

Documentation Requirements

  • Clearly document the mapping rationale in the Study Data Reviewer's Guide
  • Maintain consistent protocol version formatting across all domains
  • Include protocol version mapping decisions in data management plans

Common Pitfalls to Avoid

  • Inconsistent protocol version formatting across different domains
  • Overloading DSREFID with non-essential protocol information
  • Failing to document the mapping rationale adequately
  • Inconsistent handling of protocol versions across different studies

Conclusion

Successful protocol version mapping in SDTM disposition events requires careful consideration of the relationship between protocol versions and disposition events, clear documentation, and consistent implementation. By following these guidelines and best practices, organizations can ensure accurate and compliant data representation while maintaining traceability and supporting effective analysis.

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