Statistical programming is a key element for the management, cleaning, and analysis of clinical trial data. In particular, SAS programming has become a key tool for statistical programming in the clinical research space. In this article, we will provide an introduction to SAS programming and its benefits in clinical trials.
- $What Is SAS Language?
- $What Is Statistical Programming in Clinical Trials?
- $What Are the Benefits of SAS Statistical Programming?
- $Which Are the Alternatives to SAS Programming?
- $What Is the Future of SAS Programming in Clinical Trials?
- $Sofpromed: Expert CRO in Statistical Programming Services for Clinical Trials
What Is SAS Language?
SAS (Statistical Analysis System) is a computer programming language used for statistical analysis. It was invented by Anthony James Barr, an American programming language designer and software engineer who would later found the SAS Institute (1976-1979). He would also become President and CEO of Barr Systems, Inc.
The first appearance of the SAS language came at North Carolina State University in the 1970s, and it would be further developed during the following two decades. In fact, the incorporation of innovative statistical procedures, extra components, and the introduction of JMP, would mark the development of this programming language.
Overall, SAS helps with different types of analysis, which makes it a very practical and comprehensive statistical set of applications. For instance, SAS can take the results of studies and statistical analyses from commonly used databases and spreadsheets, and then output them in graphs, tables, and as documents in different formats.
In addition, SAS programming can analyze and establish comparisons between different variables found in extensive datasets and, on the basis of these data, predictive models can also be built.
Moreover, in contrast to SPSS —which is primarily used for statistics in other areas of study, such as demography or geostatistics— the areas on which SAS mainly focuses are raw scientific data, automated machine learning, and enterprise.
Certainly, SAS has proven to be a leader in analytics. The reason for this conception can be explained by its substantial impact on computer data analysis in government, education, as well as a great variety of areas and industries for data mining and the handling of related data.
What Is Statistical Programming in Clinical Trials?
Statistical programming is an essential component for pharmaceutical product development. Indeed, the data validated during the course of clinical trials need to be transferred and turned into interpretable information to proceed with biostatistical analysis and further clinical procedures.
Before a new drug is released onto the market, it is vital to comprehend the extent to which this product is secure, reliable, and effective. After all, we must remember that human health and life are at stake.
Bearing this in mind, statistical programming has as a principal purpose to facilitate cooperation and common understanding of clinical trial reporting. It does this by creating analysis datasets as well as tables, listings, and figures (TLFs).
In effect, the longevity and success of any organization largely depends on the accessibility, interoperability, and reusability of their data, which should be prioritized as an asset. [1]
What Are the Benefits of SAS Statistical Programming?
So far, we have learnt some key concepts and definitions regarding SAS statistical programming thanks to the overview offered in the preceding paragraphs. In this section, we will discuss the advantages of using this advanced statistical tool.
There are many reasons for employing SAS statistical programming. Here is a list of its main benefits: [2]
- Easy to learn by anyone since the SAS coding is based on simple statements.
- Ability to handle considerable large databases.
- Easy to debug as the log window clearly indicates the error that has to be solved.
- Thorough testing and analysis of the algorithms deployed in the SAS program.
- SAS intuitive customer support to handle all types of problems.
- Data in SAS is entirely secure and confidential; that is why it is regarded as a very trusted source.
- Its SAS GUI (Graphical User Interface) has a number of exceptionally useful tools, such as graphs, plots, and a well-stocked library.
- The output is entirely correct owing to the great deal of time invested in SAS development.
- Huge job prospects since a growing number of professionals are required to have SAS knowledge to enter in the analytics industry.
Due to the advantages discussed above, SAS statistical programming has clearly become a worldwide standard for the conduct of clinical trials.
Which Are the Alternatives to SAS Programming?
Even though SAS programming has proven to be very reliable and efficient, there are some alternative options capable of performing the same task as well.
The first alternative to SAS programming is Python, which is a simple, general-purpose, high-level, dynamic, and interpreted programming language. It helps programmers develop logical code and applications for small as well as large-scale projects by supporting an object-oriented approach. Moreover, it provides countless high-level data structures.
Furthermore, in spite of it being fairly easy to learn, Python is a remarkably powerful and polyvalent scripting language. Because of that, it is used in various areas, among which are: data science, data mining, artificial intelligence, web applications, and software development. As a result, Python has become one of the most popular and extensively used programming languages.
The other programming language that is going to be discussed is R. Like the previous languages, it is aimed at helping with statistical computing and graphics. It runs on Windows, MacOs, as well as a great variety of UNIX platforms.
Specifically, R is gaining popularity within the statistics community on account of its offering of a broad variety of graphical and statistical techniques that enable data collection, analysis, and drawing practical conclusions. By way of illustration, some statistical techniques provided by R include classical statistical tests, linear and nonlinear modelling, time-series analysis, clustering, and classification.
Ultimately, more alternative languages for statistical computing and graphics are Java, SPSS, SQL, Scala, MATLAB, among others.
What Is the Future of SAS Programming in Clinical Trials?
The fact that SAS programming has contributed greatly to the field of data science is beyond question. Nevertheless, some people may be wondering whether it will continue to hold ground in clinical research in the coming years.
Actually, there is no reason to believe that SAS programming is going to disappear within the foreseeable future. As a matter of fact, SAS software has emerged as a leading programming language thanks to its immense range of statistical functions, learner-friendly GUI, and unparalleled quality of technical support.
Due to its efficacy and simple handling, SAS is currently one of the preferred technologies for analyzing and understanding customer needs and requirements. As a result, not only are SAS competences in high demand, but their acquisition usually means an increase of the SAS programmer salary by more than 17% in the market.
Additionally, aside from SAS products, all the new developments related to SAS and its new platform should not go unnoticed either. They will allow the coding of SAS even with other programming languages —such as Python, Java or Lua— and SAS will provide the translation.
Lastly, it is also important to highlight that pharma companies have invested significant amounts of time and money to build SAS macros for reporting and analysis. Therefore, together with drug regulators that have validated these clinical trial outputs, such as FDA, they may be reluctant to replace SAS with other alternative tools as it would entail throwing away any of the great work that they have done.
In the light of the foregoing, it is not unreasonable to think that SAS programming still has a promising future ahead. In all likelihood, it will continue to be a key standard for statistical programming in the area of health over the short and long term.
Sofpromed: Expert CRO in Statistical Programming Services for Clinical Trials
Sofpromed is a biometrics clinical research organization (CRO) specialized in statistical programming services for clinical trials. We provide SAS statistical programming capabilities for biotech and pharma companies conducting phase I-IV clinical studies across multiple therapeutic areas. Our services include the generation of tables, figures, and listings (TFLs), SDTM and ADaM services, dataset derivation, database standardization for FDA submissions, data validation and transfers, and the development of define.xml, among others.
References:
[1] Harper Forbes, Hoffmann-La Roche Limited. 2020. “Statistical Programming in the Pharmaceutical Industry: Advancing and Accelerating Drug Development.” SAS Global Forum 2020, p. 1-10. [Accessed 19 January 2022]
[2] JavaTpoint. “Advantages and Disadvantages of SAS Programming Language.” [Accessed 21 January 2022]
