Advanced SDTM Programming Techniques for SAS Programmers

As an experienced SAS programmer working with the Study Data Tabulation Model (SDTM), it's crucial to stay updated with the latest programming techniques. Whether you're tasked with building SDTM domains from scratch or optimizing existing code, there are several advanced concepts that can improve your workflows and output. In this post, we’ll explore some techniques that can help you overcome common SDTM challenges and boost efficiency in handling clinical trial data.

1. Efficient Handling of Large Datasets

When dealing with large clinical datasets, speed and efficiency are key. One method to optimize SDTM domain generation is to reduce the data footprint by eliminating unnecessary variables and duplicative observations. Consider the following approaches:

Removing Duplicate Observations

Duplicate records can slow down the processing of datasets and cause inaccuracies in reporting. To remove duplicates, you can use the PROC SQL, DATA STEP, or PROC SORT methods. Here's a quick example using PROC SORT:

proc sort data=mydata nodupkey;
    by usubjid visitnum;
run;

This example ensures that only unique records for each usubjid and visitnum combination are retained, eliminating potential redundancy in your dataset.

2. Mastering Macro Variables for Flexibility

Utilizing macro variables efficiently can significantly improve your code's flexibility, especially when working across multiple SDTM domains. Macro variables allow you to automate repetitive tasks and reduce the risk of human error. Here’s an example using macro variables to generate domain-specific reports:

%macro create_sdtm_report(domain);
    data &domain._report;
        set sdtm.&domain.;
        /* Apply domain-specific transformations */
    run;
%mend;

%create_sdtm_report(DM);
%create_sdtm_report(LB);

In this case, the macro dynamically generates SDTM reports for any domain by passing the domain name as a parameter, minimizing manual interventions.

3. Managing Demographics with the DC Domain

The Demographics as Collected (DC) domain often presents unique challenges, particularly when distinguishing it from the standard Demographics (DM) domain. While DM represents standardized data, DC focuses on the raw, collected demographic details. Here's an approach to manage these domains efficiently:

data dc_domain;
    set raw_data;
    /* Capture specific collected demographic data */
    where not missing(collected_age) and not missing(collected_gender);
run;

In this case, the code filters out any missing collected data to ensure the DC domain contains only records with complete demographic information.

4. Debugging SDTM Code with PUTLOG

Efficient debugging is crucial, especially when dealing with complex SDTM transformations. The PUTLOG statement in SAS is a simple yet powerful tool for tracking errors and debugging data issues.

data check;
    set sdtm.dm;
    if missing(usubjid) then putlog "ERROR: Missing USUBJID at obs " _n_;
run;

In this example, the PUTLOG statement flags records where the USUBJID variable is missing, making it easier to spot and address data quality issues during SDTM creation.

5. Advanced Array Processing for Repeated Measures

In certain domains like Vital Signs (VS) or Lab (LB), handling repeated measures for patients across multiple visits is common. Using arrays can help streamline this process. Here’s a basic example of using an array to process repeated lab measurements:

data lab_repeated;
    set sdtm.lb;
    array lb_vals{3} lbtestcd1-lbtestcd3;
    do i=1 to dim(lb_vals);
        if lb_vals{i} = . then putlog "WARNING: Missing value for LBTESTCD at " _n_;
    end;
run;

This code uses an array to loop through repeated lab test results, ensuring that missing values are flagged for review. Such array-based techniques are essential when processing large, multidimensional datasets in SDTM programming.

6. Best Practices for CDISC Validation and Compliance

To ensure SDTM datasets are CDISC compliant, it’s vital to validate your datasets using tools like Pinnacle 21 or OpenCDISC. These tools check compliance against SDTM standards, flagging any inconsistencies or issues.

Make sure to incorporate validation steps into your workflows regularly. This can be done by running the validation tool after each major dataset creation and by including clear annotations in your programming code to ensure traceability for audits.

Conclusion

Advanced SDTM programming requires a mix of technical expertise and strategic thinking. Whether you are optimizing large datasets, automating repetitive tasks with macros, or ensuring CDISC compliance, staying updated with these advanced techniques will enhance your efficiency and ensure high-quality deliverables in clinical trials. Remember, SDTM programming is not just about writing code—it's about delivering accurate, compliant, and reliable data for critical decision-making.

For more tips and tutorials, check out other PharmaSUG resources or SAS support!