The 3 Best Statistical Tools for Clinical Data
Clinical data analysis plays a crucial role in medical research, healthcare decision-making, and advancing our understanding of diseases and treatments. To derive meaningful insights from clinical data, we rely heavily on statistical tools. Selecting the appropriate statistical tool depends on several factors, including what your research group is focused on, your budget, your groups’ comfort with coding, your regulatory considerations, and the degree of customization you are looking for in your analyses.
Here are the three best statistical tools to use with clinical data:
Statistical Analysis System (SAS) is the industry standard for clinical data analysis. It is regulatory-compliant, has pre-built procedures, and offers documentation, support, and validation tools. It is very user-friendly, with a graphical user interface that makes it easy for non-technical users; however, it has significant licensing and maintenance fees, and it is less flexible than one of the programming languages.
Python is an open-source, general purpose programming language that is known for its ease of learning and broad user community, making it accessible for new users. It has no license fee and the ability to read data in a variety of formats including XML, JSON, ORC, EXCEL, CSV, and TSV files. However, it does not have built-in tools for statistical analysis (you will need external libraries) and may be computationally slower.
R is an open-source, cross-platform statistical programming language with extensive tools for data analysis and visualization. It is free with a large user community but has a steeper learning curve and is not as versatile as Python.
Selecting the appropriate statistical tool is essential for ensuring accuracy, reliability, and relevance in clinical studies. It is important to clearly understand the needs and capabilities of the clinical analysis team to choose the correct tool, which will uncover meaningful insights, enhance evidence-based decision-making in healthcare, and ultimately improve patient outcomes.