Q & A
Statistical Programming & Biostatistics

Q5. Which programming languages and statistical platforms are supported?

Programming and biostatistics use multiple types of programming languages and platforms based on the type of data presented in the analysis. The analytical method used, and the types of research being conducted are,

1. R

  • R is often the language of choice for statistical computing and graphics. R has many libraries available to perform regression analysis, multivariate analyses (more than one variable), survival analysis, machine learning, and create beautiful graphics.
  • In addition to these specific analyses, it is flexible enough to allow you to work with both clinical and research data.

2. SAS

  • SAS is primarily utilized during clinical trials and during the FDA submission process, so providers rely on its reproducibility and reliability in terms of maintaining the FDA and ICH standards.
  • This is due to SAS’ many features that enable providers to manage their data, create support for statistical modelling, generate reports and perform advanced analytics.

3. Python

  • Python supports an extensive number of libraries including Pandas, NumPy, SciPy, Stats Models and Scikit-learn that allow programmers to manipulate, statistically analyse and create machine learning models.
  • Its versatility allows for integration with Databases, Dashboards and Automation Workflows.

4. SPSS

  • SPSS is an easy-to-use program that is commonly used in Social Science, Healthcare Research and Survey Analyses.
  • It can be used to get descriptive statistics, test hypothesis, develop regression models, and create visually appealing graphic representations of data.

5. Stata

  • Stata is most frequently used, but not exclusively, for Analytical Epidemiology and Health Economics as well as analysing Longitudinal Data.
  • Stata includes several comprehensive features that provide users with excellent support for data management, statistical modelling and developing Reproducible Programming (R).

6. Other platforms

  • Other support systems include MATLAB for Computational Modelling, JMP for Interactive Data Analysis, SQL or Database Integration for Collating / Managing Large Scale Datasets.