| Yoganand Budumuru |
As clinical trial data analysis continues to evolve, the choice of software tools used in statistical analysis plays a crucial role in shaping the efficiency, flexibility, and reproducibility of research outcomes. Pharmaceutical companies and Contract Research Organizations (CROs) are facing significant challenges in deciding between two prominent tools used in the statistical analysis of clinical trial data: Statistical Analysis System (SAS) and Python. There is no evidence that prior research has been conducted to compare the two powerful tools in the context of statistical analysis of clinical research.
Historically, regulatory agencies such as the FDA and others have preferred SAS for clinical trial submissions. Therefore, this proprietary software has long been the industry standard due to its robust, extensive feature set, and regulatory compliance (NoyMed, 2024). However, Python, an open-source programming language, presents a well-supported alternative with its versatility, cost-effectiveness, integration capabilities, and vibrant ecosystem of libraries tailored for data analysis (Kabeer R, 2024).
Programmers who are familiar with using SAS's unique syntax, processes, and workflows may find it difficult to migrate to Python whose methods differ. Individuals with little to no Python programming ability may face challenges due to the Python learning curve. In this context, this paper will provide a comparative analysis of SAS and Python with a focus on insights for future directions. This comparative analysis examines key factors influencing the choice between SAS and Python in the context of clinical trial data analysis. Examples of descriptive, inferential, and other important statistical analyses using SAS and Python will be discussed in detail. |