Top 10 Statistical Tools Used in Medical Research

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March 8, 2021

Top 10 statistical tools used in medical research is authored by Kolabtree freelance statistician Kingsley Okwuoma, who writes about the top statistical tools used in medical research and clinical data analysis.

There is a vast universe of statistical tools used in medical research. These tools get the job done in similar ways, but the differences lie in the ease of use and presentation as differences in licensing (proprietary or not), interface (point and click or command line), and cost (free or paid). These tools handle the end-to-end processes of collecting, organizing, analyzing, and interpreting statistical data. Let’s look at the top 10 statistical tools used in medical research by scientists, physicians, and industry R&D professionals.

  1. Stata

Stata is a complete toolbox that provides a data management capability, data analysis, and a colorful graphical interface. Stata can be termed as the policy statistical software common to institutions, including international organizations like the United Nations, governments, and academicians for Public health, Economics, Social Work, and Medicine. It remains the most powerful software available in the analytics space. The name Stata is a syllabic abbreviation of the words statistics and data and was released in 1985 and then the graphical user interface option in 2003.

  1. R

R is an open source statistical software tool that is well equipped to handle, visualization, analysis, and aspects of machine learning ‘heavy computing’ and it’s strictly a programming ‘command-line interface (CLI) software tool though relatively new in the user space, R now commands a strong fan base, boasting over 6000 packages, contributed by data scientists, bioinformatics and medical researchers, covering an expanse of disciplines from cancer research, clinical analysis, molecular biology, phylogeny, to meta-analysis.

  1. GraphPad Prism

GraphPad Prism is popular among biologists in academia and industry. It also comes with functionalities that allow researchers to perform laboratory research and clinical trial test using the t-test, one-way ANOVA, contingency table, survival analysis, and probability models like the logistic regression model.

The software, unlike any other, comes with an interpreted result analysis page after the estimates have been produced. The language is easy to understand with less technicalities. The software also has an in-built automated feature that combines the analysis and the graphical output in one snapshot – which adds to the pertaining behavior of automatic re-analysis of data in cases where any of the data points are altered, all on runtime without any need to redo the analysis performed or graph drawn.

  1. SAS

SAS is the bedrock of advanced analytics with functionalities that cuts across a diverse array of scientific and engineering enterprises and organizations. The development of SAS (Statistical Analysis System) began in 1966 by Anthony Bar of North Carolina State University and later joined by James Goodnight. The National Institute of Health-funded this project with the goal of analyzing agricultural data to improve crop yields.

Some of the down features or benefits of SAS are linked to its size and proprietary license ownership. Key among these is the time to implementation of new ideas and methods and the technicalities in the documentation process. SAS has gained popularity among Financial Services, Government, Manufacturing, and Health, and Life Sciences.


The initial version of SPSS was developed in 1968 until IBM’s acquisition in 2009. IBM SPSS is quite comprehensive and serves as the go-to tool for almost all disciplines and professionals. The software has depth in capability with an easy-to-use graphical user interface function. However, the software works best for researchers who have a basic knowledge of statistics, most especially data elements like data measurements, data types identification, variable assignment and coding, and case selection.


MATLAB (The Mathworks) was released in 1984. MATLAB is a complete command-line interface (CLI) or programming language used by scientists and engineers. As with R, the learning path is steep, and you will be required to create your own code at some point. A plentiful number of toolboxes are also available to help answer your research questions (such as EEGLab for analyzing EEG data). The difficult-to-use feature is complemented by a vast array of statistical methods and flexibility in terms of what the software can handle. MATLAB gained popularity among scientists in the areas of engineering, numerical analysis, linear algebra, and image processing.

Also, there is a list of unpopular statistical software tools out there that do well in term of ease of use, with effective point and click features.

  1. JMP

JMP combines powerful statistics with dynamic graphics, in memory and on the desktop. Its interactive and visual paradigm enables JMP to reveal insights that are impossible to gain from raw tables of numbers or static graphs. Originally stood for ‘John’s Macintosh Program’ with five bespoke products: JMP, JMP Pro, JMP Clinical, JMP Genomics, and JMP Graph Builder App.

  1. Minitab

Minitab offers a range of both basic and fairly advanced statistical tools for data analysis and was developed in 1972 from OMNITAB 80 which is a light version. Similar to GraphPad Prism, commands can be executed through both the GUI and scripted commands, making it accessible to novices as well as users looking to carry out more complex analyses.

  1. Statistica

Statistica is a suite of analytical software tools originally developed by StatSoft and acquired by Dell in 2014 and TIBCO entering the agreement to buy in 2017. Statistica is great with data management, analysis, visualization, data mining, and machine learning.

  1. Excel

Microsoft Office Excel is originally developed to handle data management Without any need of introduction, Microsoft Corp Excel is used widely in statistical analysis per the dataset taken for this review. The program has a wider reach and knowledge of use is quite widespread that the amount of unknown is very less about the way-of-use and thus the ease-of-use reaches the highest among the reviewed software.

Excel also has an add-in called Meta-Essentials, MetaXL, and MetaEasy which adds the ability to perform meta-analytical statistics with excel as a base.

There are several factors that affect the outcome of the analysis, including sample size, data collection methods, choice of test used, methodology, and more. Statistical analysis needs to be carefully done by experts to obtain reliable results. Hiring an experienced freelance medical statistician or biostatistics consultant can help you save time and energy while focusing on your research.

About the author

Kingsley Ukwuoma is an experienced statistician and data analyst on Kolabtree, providing quantitative research support. He has more than 10 years+ cognate experience in input applications including R, STATA, Eviews, Python, IBM SPSS, and SAS/Excel with industry applications in health science and analytics, epidemiology, applied statistics, and business of global health technology and financing.



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