Mastering the SDTM Review Process: Comprehensive Insights with Real-World Examples

The process of ensuring compliance with Study Data Tabulation Model (SDTM) standards can be challenging due to the diverse requirements and guidelines that span across multiple sources. These include the SDTM Implementation Guide (SDTMIG), the domain-specific assumptions sections, and the FDA Study Data Technical Conformance Guide. While automated tools like Pinnacle 21 play a critical role in detecting many issues, they have limitations. This article provides an in-depth guide to conducting a thorough SDTM review, enhanced by real-world examples that highlight commonly observed pitfalls and solutions.

1. Understanding the Complexity of SDTM Review

One of the first challenges in SDTM review is recognizing that SDTM requirements are spread across different guidelines and manuals. Each source offers a unique perspective on compliance:

• SDTMIG domain specifications: Provide detailed variable-level specifications.
• SDTMIG domain assumptions: Offer clarifications for how variables should be populated.
• FDA Study Data Technical Conformance Guide: Adds regulatory requirements for submitting SDTM data to health authorities.

Real-World Example: Misinterpreting Domain Assumptions

In a multi-site oncology trial, a programmer misunderstood the domain assumptions for the "Events" domains (such as AE – Adverse Events). The SDTMIG advises that adverse events should be reported based on their actual date of occurrence, but the programmer initially used the visit date, leading to incorrect representation of events.

2. Leveraging Pinnacle 21: What It Catches and What It Misses

Pinnacle 21 is a powerful tool for validating SDTM datasets, but it has limitations:

• What it catches: Missing mandatory variables, incorrect metadata, and value-level issues (non-conformant values).
• What it misses: Study-specific variables that should be excluded, domain-specific assumptions that must be manually reviewed.

Real-World Example: Inapplicable Variables Passing Pinnacle 21

In a dermatology study, the variable ARM (Treatment Arm) was populated for all subjects, including those in an observational cohort. Since observational subjects did not receive a treatment, this variable should have been blank. Pinnacle 21 didn’t flag this, but a manual review revealed the issue.

3. Key Findings in the Review Process

3.1 General Findings

• Incorrect Population of Date Variables: Properly populating start and end dates (--STDTC, --ENDTC) is challenging.
• Missing SUPPQUAL Links: Incomplete or incorrect links between parent domains and SUPPQUAL can lead to misinterpretation.

Real-World Example: Incorrect Dates in a Global Trial

In a global cardiology trial, visit start dates were incorrectly populated due to time zone differences between sites in the U.S. and Europe. A manual review of the date variables identified these inconsistencies and corrected them.

3.2 Domain-Specific Findings

• Incorrect Usage of Age Units (AGEU): Misuse of AGEU in pediatric studies can lead to incorrect data representation.
• Inconsistent Use of Controlled Terminology: Discrepancies in controlled terminology like MedDRA or WHO Drug Dictionary can cause significant issues.

Real-World Example: Incorrect AGEU in a Pediatric Study

In a pediatric vaccine trial, the AGEU variable was incorrectly populated with "YEARS" for infants under one year old, when it should have been "MONTHS." This was not flagged by Pinnacle 21 but was discovered during manual review.

4. Optimizing the SDTM Review Process

To conduct an effective SDTM review, follow these steps:

• Review SDTM Specifications Early: Identify potential issues before SDTM datasets are created.
• Analyze Pinnacle 21 Reports Critically: Don’t rely solely on automated checks—investigate warnings and study-specific variables manually.
• Manual Domain Review: Ensure assumptions are met and variables are used correctly in specific domains.

5. Conclusion: Building a Holistic SDTM Review Process

By combining early manual review, critical analysis of automated checks, and a detailed review of domain-specific assumptions, programmers can significantly enhance the accuracy and compliance of SDTM datasets. The real-world examples provided highlight how even small errors can lead to significant downstream problems. A holistic SDTM review process not only saves time but also ensures higher data quality and compliance during regulatory submission.

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Revolutionizing SDTM Programming in Pharma with ChatGPT

By Sarath

Introduction

In the pharmaceutical industry, standardizing clinical trial data through Study Data Tabulation Model (SDTM) programming is a critical task. The introduction of AI tools like ChatGPT has opened new opportunities for automating and enhancing the efficiency of SDTM programming. In this article, we will explore how ChatGPT can assist programmers in various SDTM-related tasks, from mapping datasets to performing quality checks, ultimately improving productivity and accuracy.

What is SDTM?

SDTM is a model created by the Clinical Data Interchange Standards Consortium (CDISC) to standardize the structure and format of clinical trial data. This model helps in organizing data for submission to regulatory bodies such as the FDA. SDTM programming involves mapping clinical trial datasets to SDTM-compliant formats, ensuring data quality, and validating that the data follows CDISC guidelines.

How ChatGPT Can Enhance SDTM Programming

ChatGPT can be a game-changer in SDTM programming, providing real-time support, automation, and solutions for common challenges. Here’s how it can be applied in various stages of the SDTM process:

• Assisting with Mapping Complex Datasets: ChatGPT can provide real-time guidance and suggestions for mapping non-standard datasets to SDTM domains, helping programmers to ensure compliance with CDISC guidelines.
• Generating Efficient SAS Code: ChatGPT can generate optimized SAS code for common SDTM tasks, such as transforming raw datasets, handling missing data, or applying complex business rules to ensure the data meets the regulatory standards.
• Debugging SAS Code: ChatGPT can assist in identifying bugs, suggesting ways to debug code, and improving code readability with useful tips like employing the PUTLOG statement.
• Automating Quality Control Checks: Performing quality checks on large datasets is essential in SDTM programming. ChatGPT can automate parts of this process by generating code for missing variable checks, duplicate observations removal, and ensuring that domain-specific rules are followed.
• Improving Code Readability: By suggesting best practices for writing clear and maintainable SAS code, ChatGPT can help reduce technical debt and make the code easier to review and debug, especially in collaborative settings.
• Providing Learning Support for New Programmers: For beginners in SDTM programming, ChatGPT can explain complex concepts in simple terms, provide examples, and offer real-time solutions to questions related to SDTM domains, controlled terminology, and regulatory requirements.

Practical Use Cases for ChatGPT in SDTM Programming

Let's look at a few examples where ChatGPT can offer tangible benefits in SDTM programming:

• Handling the Demographics Domain (DM): ChatGPT can guide programmers through mapping raw datasets to the SDTM DM domain, offering suggestions for handling specific data types like SUBJID, AGE, and SEX. It can also generate SAS code that adheres to CDISC standards and offers tips for validating the resulting data.
• Generating Define.XML Files: Defining metadata is critical for regulatory submission. ChatGPT can assist by generating SAS code for creating and validating Define.XML files using tools like Pinnacle 21, ensuring compliance with regulatory expectations.
• Managing Controlled Terminology: Keeping up with the latest controlled terminology versions (e.g., MedDRA, SNOMED, UNII) is essential. ChatGPT can suggest updates for domain-specific controlled terminology and provide SAS code to automate its application in SDTM datasets.

Limitations and Future Potential

While ChatGPT offers significant advantages, there are still some limitations. For instance, it lacks deep integration with SAS or Pinnacle 21, which means that users need to manually adapt ChatGPT’s suggestions to their specific environments. However, the future potential for ChatGPT to evolve into an even more intelligent assistant is immense. As AI technology advances, ChatGPT could become an essential tool for real-time error detection, domain mapping, and automating SDTM processes end-to-end.

Conclusion

ChatGPT has the potential to transform the way SDTM programming is done in the pharmaceutical industry. From guiding new programmers to automating repetitive tasks and assisting with complex coding challenges, this AI tool can significantly improve the efficiency and accuracy of SDTM workflows. As we continue to explore the capabilities of AI, the integration of tools like ChatGPT into programming environments will become an increasingly vital asset for organizations looking to streamline their clinical data management and regulatory submission processes.

Published by Sarath on [Insert Date]

Unleashing the Power of PROC DATASETS in SAS

The PROC DATASETS procedure is a versatile and efficient tool within SAS for managing datasets. Often described as the "Swiss Army Knife" of SAS procedures, it allows users to perform a variety of tasks such as renaming, deleting, modifying attributes, appending datasets, and much more, all while consuming fewer system resources compared to traditional data steps. In this article, we’ll explore key use cases, functionality, and examples of PROC DATASETS, illustrating why it should be part of every SAS programmer's toolkit.

1. Why Use PROC DATASETS?

Unlike procedures like PROC APPEND, PROC CONTENTS, and PROC COPY, which focus on specific tasks, PROC DATASETS integrates the functionalities of these procedures and more. By using PROC DATASETS, you avoid the need for multiple procedures, saving both time and system resources since it only updates metadata instead of reading and rewriting the entire dataset.

2. Basic Syntax of PROC DATASETS

The basic structure of PROC DATASETS is as follows:

PROC DATASETS LIBRARY=;
    ;
RUN; QUIT;

Here, you specify the library containing the datasets you want to modify. Commands such as CHANGE, DELETE, APPEND, MODIFY, and RENAME follow within the procedure.

3. Use Case 1: Renaming Datasets and Variables

Renaming datasets and variables is a simple yet powerful capability of PROC DATASETS. Here's an example of how you can rename a dataset:

PROC DATASETS LIBRARY=mylib;
    CHANGE old_data=new_data;
RUN; QUIT;

To rename a variable within a dataset:

PROC DATASETS LIBRARY=mylib;
    MODIFY dataset_name;
    RENAME old_var=new_var;
RUN; QUIT;

4. Use Case 2: Appending Datasets

The APPEND statement is a highly efficient alternative to using SET in a data step because it only reads the dataset being appended (the DATA= dataset), instead of reading both datasets.

PROC DATASETS LIBRARY=mylib;
    APPEND BASE=master_data DATA=new_data;
RUN; QUIT;

5. Use Case 3: Deleting Datasets

Deleting datasets or members within a library is simple with PROC DATASETS. You can delete individual datasets or use the KILL option to remove all members of a library:

PROC DATASETS LIBRARY=mylib;
    DELETE dataset_name;
RUN; QUIT;

PROC DATASETS LIBRARY=mylib KILL;
RUN; QUIT;

6. Use Case 4: Modifying Attributes

You can modify variable attributes such as labels, formats, and informats without rewriting the entire dataset:

PROC DATASETS LIBRARY=mylib;
    MODIFY dataset_name;
    LABEL var_name='New Label';
    FORMAT var_name 8.2;
RUN; QUIT;

7. Advanced Operations with PROC DATASETS

7.1. Working with Audit Trails

You can use PROC DATASETS to manage audit trails, which track changes made to datasets. For instance, the following code creates an audit trail for a dataset:

PROC DATASETS LIBRARY=mylib;
    AUDIT dataset_name;
    INITIATE;
RUN; QUIT;

7.2. Managing Indexes

Indexes help retrieve subsets of data efficiently. You can create or delete indexes with PROC DATASETS:

PROC DATASETS LIBRARY=mylib;
    MODIFY dataset_name;
    INDEX CREATE var_name;
RUN; QUIT;

7.3. Cascading File Renaming with the AGE Command

Another useful feature is the AGE command, which renames a set of files in sequence:

PROC DATASETS LIBRARY=mylib;
    AGE file1-file5;
RUN; QUIT;

Checking If a SAS Dataset is Sorted Using PROC DATASETS

In SAS, datasets often need to be sorted to facilitate various analytical operations. Sorting ensures that records are organized based on one or more variables. However, it’s important to know whether a dataset is already sorted before performing time-consuming operations like PROC SORT. Fortunately, SAS provides an efficient way to check whether a dataset is sorted by using the PROC DATASETS procedure.

Why Use PROC DATASETS to Check Sort Status?

PROC DATASETS is a powerful procedure that can manage and inspect datasets. It allows you to view metadata, including the SORTEDBY attribute, which tells you if the dataset has been sorted and by which variables. This method is faster and more efficient than unnecessarily re-sorting a dataset.

Step-by-Step Example

Let’s walk through an example where we use PROC DATASETS to check whether a dataset is sorted.

Sample SAS Code

/* Step 1: Use PROC DATASETS to inspect the dataset's metadata */
proc datasets lib=work nolist;
  contents data=your_dataset out=sorted_info(keep=name sortedby);
run;
quit;

/* Step 2: Print the output to see the SORTEDBY variable */
proc print data=sorted_info;
run;
    

Code Explanation

• proc datasets lib=work nolist; - Specifies the library (in this case, WORK) and suppresses the list of files using the NOLIST option.
• contents data=your_dataset out=sorted_info(keep=name sortedby); - Extracts the metadata for your_dataset and outputs the SORTEDBY information to a dataset named sorted_info.
• proc print data=sorted_info; - Prints the dataset to view the SORTEDBY information.

Interpreting the Output

The output dataset sorted_info will contain the following columns:

• Name: The name of the dataset (in this case, your_dataset).
• SortedBy: A list of the variables by which the dataset is sorted. If this field is empty, it means the dataset is not sorted.

Example Output

Name	SortedBy
your_dataset	var1 var2

In this case, your_dataset is sorted by the variables var1 and var2. If the SortedBy column is empty, it indicates that the dataset is not sorted.

Handling Multiple Datasets

If you need to check multiple datasets in a library, you can modify the PROC DATASETS step to inspect all datasets without specifying a particular dataset.

proc datasets lib=work nolist;
  contents out=sorted_info(keep=name sortedby);
run;
quit;

/* Print the sorted_info dataset */
proc print data=sorted_info;
run;
    

Note: The SORTEDBY attribute is only updated when a dataset is sorted using PROC SORT. If variables are added after sorting, or the dataset wasn't sorted explicitly, this attribute might not reflect the current sorting status.

Conclusion

PROC DATASETS is an indispensable tool for SAS programmers. Its efficiency and versatility allow you to manage datasets with ease, from renaming and deleting to appending and modifying attributes. By leveraging its full potential, you can streamline your SAS workflows and significantly reduce processing times.

Sources

• Ken Friedman, "Using PROC DATASETS for Efficient SAS Processing"
• David Fickbohm, "Why Use PROC DATASETS"
• Michael A. Raithel, "PROC DATASETS: The Swiss Army Knife of SAS Procedures"