Are We Ready for TA-Specific Automation?

Introduction

In clinical research, standardization is essential. It ensures consistency, regulatory compliance, and, most importantly, clear and usable data. Over the years, CDISC standards have been widely adopted to bring uniformity to clinical trial data. But as studies become more complex, especially in specialized fields like oncology and vaccines, Therapeutic Area (TA) standards have emerged to address unique data needs.

Now comes the big question: Can we automate these TA-specific standards?

Automation has brought huge improvements in clinical data standardization, especially in general SDTM and ADaM workflows. But when it comes to TA-specific domains, especially newer or evolving ones, full automation remains a work in progress.

In this blog, we explore where the industry stands today on CDISC Therapeutic Area Standards, what challenges are holding back TA-specific automation, and how we might move forward with flexible strategies.

What Are CDISC Therapeutic Area Standards?

CDISC (Clinical Data Interchange Standards Consortium) introduced TA-specific standards to provide clear modeling guidance for therapeutic areas with complex or unique data requirements.

These standards build on core SDTM and ADaM structures but include additional domains, controlled terminology, and implementation guidelines tailored to specific conditions or interventions.

Some key TA standards include:

1. Oncology: Includes domains like TU (Tumor Identification), TR (Tumor Response), and RS (Response), which support RECIST criteria.

2. Vaccines: Covers immunogenicity assessments, adverse event monitoring, and multiple timepoint evaluations.

3. As of 2025, CDISC TAUGs now cover over 45 therapeutic areas, including new additions like Pancreatic Cancer, Heart Failure, Psoriasis, and Traditional Chinese Medicine.

These domain-specific additions aim to improve data quality, harmonize submissions, and support better outcomes across trials in high-need therapeutic areas.

The Case for TA-Specific Automation

Standard automation tools and macros have already proven successful in speeding up:

1. SDTM dataset creation
2. ADaM derivation
3. Metadata validation and documentation

Applying these same principles to TA standards would:

1. Reduce manual programming effort
2. Improve consistency across studies
3. Lower the learning curve for new team members
4. Speed up regulatory submissions, especially for common indications like oncology

So, why isn’t it widely done yet?

Key Challenges in Automating TA-Specific Domains

While the benefits are clear, TA-specific automation introduces new challenges not seen in core SDTM or ADaM domains:

1. Variability in Implementation

Despite published TAUGs, sponsor-specific interpretations and data collection methods vary widely.

For example, in oncology trials:

1. Not all studies use RECIST criteria the same way
2. TU/TR/RS domains may vary in structure depending on imaging schedules
3. Some studies capture tumor assessments in custom formats or local labs

This variability makes it difficult to design one-size-fits-all automation logic.

2. Incomplete or Evolving Standards

Many TA standards are still under development or frequently updated. As a result:

1. Standards may not cover all scenarios (e.g., rare endpoints or hybrid designs)
2. Teams must often rely on custom domains (SUPPQUAL or non-standard structures)
3. Frequent updates make it hard to maintain stable automation scripts

Building reusable automation in this landscape requires a flexible and modular approach, not just hard-coded logic.

3. Custom Domains and QRS Data

A major hurdle in TA-specific automation is handling custom domains and Questionnaire, Rating, and Scale (QRS) instruments.

Challenges include:

1. Mapping non-standard questionnaires not covered in the CDISC QRS database
2. Adjusting for differences in wording, scoring, or visit structure
3. Creating domains that are “CDISC-like” but not officially defined

Without strong mapping rules or documentation, automation becomes both complex and error-prone.

Strategies for Moving Forward: How Can We Automate More, Safely?

Despite these challenges, it’s possible to build automation frameworks that adapt to TA-specific needs. At Altrid, we believe in a macro-based, metadata-driven, rules-oriented approach that balances structure and flexibility.

Here’s how:

1. Use Parameterized Macros for TA Domains

Rather than building fixed automation for each TA, create parameter-driven macros that can:

1. Adapt to variable naming conventions
2. Recognize sponsor-specific input structures
3. Apply CDISC TA logic based on metadata input

This allows for reuse across studies with minor customization.

2. Build Modular Automation Blocks

Break automation into small, task-specific modules that can be combined based on the study’s design. For example:

1. Metadata that defines TU and TR parameters
2. Rules that cross-verifies TR/TU linkage
3. Custom rules in the form of macros that checks logical mappings to verify no source data is lost in SDTM or ADaM

This modular approach keeps automation adaptable while still offering speed and standardization.

3. Create a Custom Domain Library

Since TA standards often require custom domains, maintain a validated internal library of:

1. Sponsor-approved custom rules
2. Mappings for non-standard parameters  unsupported QRS instruments
3. Historical submission examples

This helps new studies reuse proven logic, accelerating dataset creation and documentation.

4. Invest in Continuous Learning

Finally, ensure programming and data teams stay current with evolving CDISC guidance. TA standards are being updated regularly, and having a knowledgeable team ensures:

1. Correct interpretation of new guidance
2. Early identification of automation opportunities
3. Compliance with regulatory expectations

Automation can only go as far as the humans behind it allow—ongoing training is key.

So, Are We Ready for TA-Specific Automation?

The short answer is: we’re getting there.

The foundations for automation are in place, especially for well-established TA standards like oncology and vaccines. But challenges around variability, evolving guidance, and complex custom data still limit how far automation can go without thoughtful design.

At Altrid, we approach TA-specific automation not as a shortcut, but as a strategy—balancing speed, flexibility, and compliance. By building smart macros, modular code, and reusable assets, we help clients navigate complex therapeutic areas with more confidence and less rework.