Good Programming Practices in Clinical Trials Programming (SAS & R)

Ensuring Accuracy, Compliance, and Efficiency in Clinical Data Programming

In the highly regulated and quality-sensitive world of clinical research, programming plays a pivotal role in transforming raw data into meaningful statistical outputs for regulatory submissions, safety reviews, and scientific publications. Whether you're using SAS—the gold standard in clinical trials—or R, the emerging open-source powerhouse, good programming practices are critical to ensuring compliance, traceability, and reproducibility.

This article explores key principles and best practices in clinical trials programming, with a focus on SAS and R, tailored to professionals involved in clinical data management, statistical programming, and biostatistics.

Highlighted example of good programming practices, showcasing clean and well-organized code structure.

Why Good Programming Practice Matters in Clinical Trials

Unlike general-purpose programming, clinical trial programming must satisfy:

• Regulatory requirements (e.g., FDA, EMA, PMDA)

• Clinical data standards (e.g., CDISC – SDTM, ADaM)

• Validation and audit readiness

• End-user clarity and reproducibility

Poor practices can result in misleading analyses, regulatory rejections, and compliance breaches, leading to costly delays or data integrity issues.

“Write code for others to read, not just for machines to execute.”

Key Principles of Good Programming Practice

1. Clarity and Readability

• Use meaningful variable and macro names (e.g., trt_start_date vs. a1)

• Maintain consistent indentation and alignment

• Break down large programs into modular sections or macros

• Add clear and concise comments explaining logic, assumptions, and purpose

2. Structured Program Layout

Typical structure of a clinical trial program:

• Header Section: Author, purpose, date, protocol ID

• Input Section: Dataset paths, libraries, parameters

• Processing Section: Data cleaning, transformations, derivations

• Output Section: Datasets, tables, listings, figures (TLFs)

• Footer Section: Logs, notes, exit code

3. Validation and Reproducibility

• Use double programming or independent reviewer validation

• Write testable code with intermediate checks and log messages

• Save output logs and version control scripts

4. Modular and Reusable Code

• Store macros/functions centrally to ensure consistency

• Create template programs for standard outputs like AE listings, demographic tables

• Avoid hardcoding; use macro variables or parameters to make code flexible

5. Standardization (CDISC, ADaM, SDTM)

• Follow CDISC-compliant programming for SDTM and ADaM datasets

• Use controlled terminology (e.g., MedDRA, WHODrug)

• Validate datasets using Pinnacle 21 (P21) or OpenCDISC validator

SAS-Specific Best Practices

SAS has long been the preferred tool for regulatory submissions. Some key practices include:

• Use PROC SQL for complex joins and merges with traceability

• Leverage DATA steps for sequential logic and transformation

• Utilize SAS macros for automating repetitive tasks (e.g., creating similar TLFs)

• Validate log files for warnings, errors, and uninitialized variables

• Prefer libname references over hardcoded file paths for portability

SAS Example:

%let trtgrp = TRT01P;

data adsl;

set sdtm.dm;

where safety_flag = 'Y';

trt_group = &trtgrp;

run;

R Programming Best Practices in Clinical Trials

R is increasingly used in exploratory analysis, visualization, and even production reporting. Best practices include:

• Use tidyverse packages (dplyr, ggplot2, readr, etc.) for clean, readable code

• Document your scripts using Roxygen2 for reproducible functions

• Use projects and environments (renv, packrat) for version control

• Adopt rmarkdown or Quarto for dynamic reporting of TLFs

• Validate code and outputs using unit testing (testthat) or peer review

R Example:

library(dplyr)

adsl <- dm %>%

filter(SAFETY_FLAG == "Y") %>%

mutate(TRT_GROUP = TRT01P)

Programming Compliance and Audit Readiness

Clinical programming outputs are subject to inspection by regulatory agencies. Ensure:

• Traceability from source data to outputs (annotated shells help)

• Program documentation including metadata and specifications

• Logs and results are archived and version-controlled

• Compliance with 21 CFR Part 11 for electronic records

Common Mistakes to Avoid

• Hardcoding treatment arms or subject IDs

• Ignoring missing values handling

• Not saving intermediate datasets for traceability

• Re-using old programs without adapting to current specs

• Not checking the SAS log or R console for warnings/errors

Building Long-Term Programming Excellence

To sustain and grow as a clinical trial programmer:

• Stay updated with industry standards (CDISC, ICH, regulatory updates)

• Attend workshops, conferences, and online learning programs

• Join professional networks like PHUSE, ASA, R Consortium, or CDISC

• Document your code for team knowledge sharing and audit readiness

• Learn both SAS and R to stay flexible and relevant

Conclusion

Good programming practice is not just about writing code—it’s about writing reliable, readable, and regulatory-compliant code that ensures data integrity, supports scientific validity, and accelerates drug development timelines. Whether you're using SAS for regulatory submissions or R for advanced analytics, mastering these principles will elevate your value as a clinical research professional.