So you want to be a Clinical Trials Programmer……

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From Raw Data to Regulatory Submission: A Clinical Trials Programmer’s Journey with SAS

In clinical research, the journey from raw data to regulatory submission is complex, highly regulated, and critically important. Every dataset must be standardized, traceable, and analytically sound. The SAS Clinical Trials Learning Subscription is designed to guide professionals through this journey—equipping them with the tools, techniques, and domain knowledge needed to succeed.

Let’s explore how this learning path empowers clinical trials programmers through a detailed case study and a deep dive into the two pillars of clinical data standardization: SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model).

The learning journey as described can be accessed from the SAS Clinical Trials Learning subscription.

A Day in the Life: Priya, Clinical Trials Programmer

Priya works at a global CRO supporting oncology trials. Her role is to transform raw clinical data into submission-ready datasets that meet FDA and CDISC standards. Her day is structured around the two key deliverables: SDTM for data tabulation and ADaM for analysis.

Morning Focus: Study Data Tabulation Model (SDTM)

SDTM is the foundation of regulatory submission. It organizes raw clinical data into standardized domains that describe trial design, subject demographics, adverse events, lab results, and more.

What Priya Does

Priya starts by reviewing raw data from the EDC system. Her goal is to convert this into SDTM domains using SAS.

How the Learning Subscription Helps

Through the SAS Programming for Clinical Trials 1: SDTM course, Priya learns to:

• Understand CDISC and SDTMIG: She navigates the SDTM Implementation Guide to ensure every domain aligns with FDA expectations.
• Build Core Domains: She creates DM (Demographics), AE (Adverse Events), LB (Lab Results), and EX (Exposure) domains using SAS procedures and SQL joins.
• Create Supplemental Qualifiers: Using SUPPDM and SUPPAE, she adds non-standard variables while maintaining compliance.
• Ensure Traceability: She uses metadata mapping to link raw data to SDTM variables, ensuring auditability.

Real-World Impact

Priya’s SDTM datasets are validated using Pinnacle 21 and submitted to the FDA. Her work ensures that reviewers can easily interpret the trial data, accelerating the approval process.

Afternoon Focus: Analysis Data Model (ADaM)

ADaM datasets are designed for statistical analysis. They provide derived variables, flags, and structures that support efficacy and safety evaluations.

What Priya Does

After SDTM is complete, Priya builds ADaM datasets like ADSL (Subject-Level Analysis) and ADAE (Adverse Events Analysis). These datasets feed directly into TLFs (Tables, Listings, and Figures) used in clinical study reports.

How the Learning Subscription Helps

In the SAS Programming for Clinical Trials 2: ADaM course, Priya learns to:

• Apply ADaMIG Standards: She uses the ADaM Implementation Guide to structure datasets for analysis.
• Create Derived Variables: She calculates change from baseline, treatment-emergent flags, and study day variables.
• Use BDS and OCCDS Structures: She builds datasets that support longitudinal and occurrence-based analyses.
• Automate with Macros: She writes SAS macros to streamline derivations and ensure consistency across studies.

Real-World Impact

Priya’s ADaM datasets are used by statisticians to generate key efficacy and safety outputs. Her work directly supports regulatory decision-making and publication of trial results.

Visual Workflow: From Raw Data to Submission

Here’s a visual representation of Priya’s workflow, powered by the SAS Clinical Trials Learning Subscription:

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The Learning Path That Powers It All

The SAS Clinical Trials Learning Subscription is structured to build expertise step-by-step:

1. SAS Programming 1: Essentials
   
   • Learn to write and run SAS code.
   • Import, explore, and prepare data.
   • Use SQL to query and join tables.

2. SAS Programming 2: Data Manipulation Techniques
   
   • Master the DATA step and SAS functions.
   • Merge, reshape, and transform datasets.

3. SAS SQL 1: Essentials
   
   • Use SQL for subsetting, summarizing, and joining data.
   • Create views and macro variables from queries.

4. SAS Macro Language 1: Essentials
   
   • Automate repetitive tasks.
   • Build dynamic, reusable code for clinical workflows.

5. SAS Programming for Clinical Trials 1: SDTM
   
   • Learn CDISC standards and SDTM domain creation.
   • Map raw data to regulatory formats.

6. SAS Programming for Clinical Trials 2: ADaM
   
   • Build analysis-ready datasets.
   • Apply ADaM structures and automate derivations.

Who Should Enroll?

This subscription is ideal for:

• Clinical data managers
• Biostatisticians
• SAS programmers in life sciences
• Regulatory affairs professionals
• Anyone transitioning into clinical trials programming

Whether you're new to SAS or looking to specialize in clinical research, this learning path offers the depth and flexibility to meet your goals.

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