Statistical Software for Healthcare Research

Healthcare research increasingly depends on statistical analysis. Whether we are comparing two treatments, looking for risk factors for a disease, estimating survival or performing a meta-analysis, we eventually need some way to analyse the data.

There are many statistical software packages available for this purpose. Some of the names that commonly come up in medical research are R, Stata, SPSS, SAS, RevMan and GraphPad Prism. They can all perform statistical analyses, but they are not really designed for the same purpose.

For someone starting research, this can be confusing. Which software should you learn? Is R better than SPSS? Do you need Stata for epidemiological research? Is SAS necessary for clinical trials? And if you are doing a systematic review and meta-analysis, is RevMan enough?

The answer is that there is no single software that is best for every situation. Each has its own strengths and weaknesses, and the right choice depends on the type of research, the statistical methods required, the researcher's experience and sometimes the resources available.

More importantly, statistical software is only a tool. A computer can perform a statistical test in seconds, but it cannot decide whether that test is appropriate for your research question. It can produce a p-value, but it cannot tell you whether the finding is clinically important. Understanding statistics is therefore more important than knowing how to operate any particular software.

In this post, we will look at some of the statistical software commonly used in healthcare research and discuss what each of them is good at, where they have limitations and when you might want to use one over another.

Why Do We Need Statistical Software?

A healthcare study often starts with a simple question. Does one treatment work better than another? Is smoking associated with a particular disease? Does a laboratory value predict mortality? What is the average blood pressure in a group of patients?

To answer these questions, we collect data. But having a dataset is not enough. We need to organise the data, summarise it, analyse it and interpret the results. This is where statistical software becomes useful.

For a small dataset, some calculations can be done by hand or with a simple calculator. But clinical research datasets can contain hundreds or thousands of patients, with many variables for each patient. Performing the calculations manually quickly becomes impractical and, more importantly, increases the chance of making mistakes.

Statistical software can perform these calculations quickly and consistently. It can calculate means and medians, compare groups, estimate confidence intervals, perform hypothesis tests and fit statistical models. More advanced software can also perform survival analysis, regression modelling, longitudinal analysis, meta-analysis and many other specialised analyses.

But statistical software does more than perform statistical tests.

Data management

Before analysing data, we usually need to clean and organise it. Patients may have missing values, incorrectly entered measurements, duplicate records or variables recorded in different formats.

Statistical software can help identify these problems and prepare the dataset for analysis.

Data analysis

Once the data are ready, software can perform the appropriate statistical analysis. For example, we might compare the mean blood pressure between two groups, calculate the odds ratio for a risk factor or estimate the hazard ratio for mortality.

Doing these calculations manually would be difficult even for a relatively small study. With statistical software, the same analysis can usually be performed in a few commands or menu selections.

Visualisation

Good research is not only about numbers. We often need to see the data.

Statistical software can produce histograms, scatter plots, box plots, survival curves, forest plots and many other types of graphs. Visualising the data can reveal patterns that may not be obvious from a table of numbers.

Reproducibility and verifiability

Another important advantage is that statistical software can make an analysis reproducible.

Suppose we analyse a dataset and obtain a result. A few months later, we discover an error in one variable or receive additional patients. If the analysis was performed using a documented sequence of commands, we can correct the data and run the same analysis again.

The same applies when another researcher wants to examine our work. If the data processing and statistical analysis have been properly documented, they can follow the same steps and verify how the reported results were obtained.

This is much more reliable than trying to remember which buttons were clicked and which options were selected.

For this reason, some statistical software allows researchers to write and save scripts or commands. The analysis can then be repeated whenever necessary.

But software does not do the thinking

There is an important limitation.

Statistical software can tell us the result of a statistical test, but it cannot determine whether we asked the right question in the first place.

For example, a software package can calculate a p-value for a t-test. It cannot know whether a t-test was appropriate for the data, whether the assumptions of the test were satisfied or whether the difference found is clinically meaningful.

In other words, statistical software makes the calculation easier. It does not necessarily make the research better.

This distinction is important when choosing statistical software. The best software is not necessarily the one with the largest number of statistical tests. It is the one that allows us to perform the analysis we need accurately, efficiently and reproducibly.

What Should You Look for in Statistical Software?

There are many statistical software packages available, but having more features does not necessarily make one software better than another. The important question is whether the software is suitable for the work you need to do.

Several factors are worth considering when choosing statistical software.

Statistical methods

The first question is whether the software can perform the statistical analysis you need.

Most commonly used software can perform basic analyses such as descriptive statistics, t-tests, chi-square tests and correlation. However, the differences become more important when the analysis becomes more complicated.

For example, if you need survival analysis, multivariable regression, mixed-effects models or advanced methods for missing data, some software may be more suitable than others.

Therefore, it is better to start with the research question and statistical method rather than starting with the software.

Ease of use

Statistical software can be broadly divided into two types. Some are mainly menu-driven, where the researcher selects an analysis from a graphical interface. Others rely more heavily on commands or programming.

Menu-driven software can be easier for beginners. You can select a test, choose the variables and obtain the result without writing code.

Programming-based software has a steeper learning curve, but it provides much greater control over the analysis. Once the commands are learned, they can also be saved and reused.

Neither approach is inherently better. The important thing is to choose a tool that you can use correctly.

Reproducibility and verifiability

The way an analysis is recorded is also important.

If an analysis consists of a series of menu selections, it may be difficult to remember exactly what was done later. Software that allows commands or scripts to be saved provides a clearer record of the analysis.

This makes it easier to reproduce the analysis, identify errors and verify how the results were obtained.

For research that will be repeated or updated frequently, this can be a major advantage.

Data management

Statistical analysis usually begins long before the first statistical test. Data need to be imported, cleaned, transformed and organised.

A software package that works well with the type and size of data you have can save considerable time.

This becomes particularly important in large observational studies where there may be thousands of patients and many variables.

Visualisation

Graphs are an important part of healthcare research. They can help us understand the data and communicate the results.

Some software is particularly good at producing publication-quality graphs, while others place more emphasis on data management or statistical modelling.

If visualisation is an important part of your work, this should be considered when choosing the software.

Community and support

A statistical software package is much easier to learn when there is good documentation and an active user community.

This is particularly important when you encounter an unfamiliar statistical problem. Being able to find examples, tutorials and discussions from other researchers can save a great deal of time.

Open-source software such as R has a particularly large community and a very extensive collection of additional packages. Commercial software such as Stata, SPSS and SAS also provides extensive documentation and established support systems.

Cost

Cost is another practical consideration.

Some software is free while others require a licence. The cost may be particularly relevant for students, individual researchers or institutions with limited resources.

However, the price of the software should not be considered in isolation. A free program may require considerable time to learn, while an institution may already have a licence for a commercial package.

The most expensive software is not necessarily the best software for a particular project.

Availability in your field

Finally, it is worth considering what is commonly used in your area of research.

If everyone in your research group uses Stata, learning Stata may make collaboration easier. If your colleagues work primarily with R, being able to share scripts and packages can be very useful.

Similarly, some fields have established preferences. SAS is widely used in pharmaceutical research and clinical trials, while RevMan is commonly encountered in systematic reviews.

This does not mean that you must use the software used by everyone else. But compatibility with your collaborators, supervisors and research community can make the research process considerably easier.

Ultimately, choosing statistical software is a balance between what you need to analyse, what you can use correctly and what allows you to produce results that are reproducible and verifiable.

R

R is a free and open-source programming language and software environment for statistical computing and data visualisation. It is widely used in research and has become particularly popular in epidemiology, biostatistics and clinical research.

Unlike traditional menu-driven software, R primarily works through commands. Instead of selecting a statistical test from a menu, the researcher writes instructions that tell R what to do.

This may sound complicated at first, but it provides considerable flexibility. Almost every part of an analysis can be controlled through code, from importing and cleaning the data to performing statistical tests and creating graphs.

R also has a large collection of additional packages developed by researchers around the world. These packages extend R and allow it to perform specialised statistical methods that may not be available in other software.

Strengths

The main strength of R is its flexibility.

R can perform simple statistical tests such as t-tests and chi-square tests, but it can also handle more advanced methods such as multivariable regression, survival analysis, mixed-effects models and many other specialised techniques.

Its package system is particularly important. There are thousands of packages available for different statistical methods and types of data. This means that researchers are not limited to the analyses included in the basic software.

R is also particularly strong for data visualisation. It can produce simple graphs as well as highly customised figures suitable for publication. The researcher has control over almost every aspect of a graph.

Another advantage is that R can be used throughout the research workflow. Data can be imported, cleaned, transformed, analysed and visualised without having to move between different programs.

Finally, R is free. Anyone can download and use it without purchasing a commercial licence. This makes it particularly attractive to students and researchers working with limited resources.

Limitations

The biggest limitation of R is its learning curve.

Because R is primarily command-based, a beginner needs to learn at least some programming before being able to use it effectively. Someone accustomed to selecting options from menus may initially find R frustrating.

The large number of available packages can also be confusing. There may be several packages that can perform similar analyses, with different syntax and approaches. Having more options does not necessarily make the decision easier.

R also gives the researcher considerable freedom. This is useful for an experienced user, but a beginner can easily perform an inappropriate analysis if they do not understand the underlying statistical concepts.

Therefore, learning R should not be thought of as learning statistics. R can perform a statistical test correctly according to the instructions given to it, but it cannot determine whether that test is appropriate for the research question.

Where is it commonly used?

R is used across almost all areas of healthcare research.

It is particularly common in epidemiology and biostatistics, where researchers may need regression models, survival analysis or other advanced statistical methods. It is also widely used in clinical research and increasingly in areas such as bioinformatics and health data science.

Because R can handle complex analyses and large datasets, it is useful when the analysis goes beyond the standard statistical tests commonly taught to beginners.

It is also increasingly used for creating reproducible research reports in which the statistical analysis, tables and figures are generated directly from the underlying data.

Reproducibility and verifiability

R has a major advantage when reproducibility and verifiability are important.

An analysis can be written as a script and saved along with the data and other relevant files. If the data are changed or additional patients are added, the same script can be run again to reproduce the analysis.

The code also provides a record of exactly what was done. Another researcher can inspect the commands and understand how the reported results were produced. This makes the analysis easier to verify and can also make errors easier to identify.

This is one of the important differences between a code-based workflow and an analysis performed entirely through a graphical interface. Instead of remembering which options were selected, the researcher has a written record of the analysis.

Of course, reproducibility still depends on good research practices. Saving code alone does not guarantee that an analysis is correct or reproducible. The data, packages, software versions and other relevant parts of the workflow may also need to be documented.

Who should consider using it?

R is particularly suitable for researchers who want a flexible, powerful and reproducible statistical environment and are willing to learn programming.

It is an especially good choice for researchers involved in epidemiology, biostatistics or clinical research who expect to perform statistical analysis regularly or need methods that are not easily available in simpler software.

For someone who only needs to perform a few basic analyses, learning R may be more effort than necessary. But for researchers who plan to do substantial statistical work, the initial learning curve can be a worthwhile investment.

In short, R is powerful, flexible and free, but it requires learning. It is not necessarily the easiest statistical software to start with, but it can become one of the most capable tools in a healthcare researcher's toolbox.

Stata

Stata is a commercial statistical software package widely used for data management, statistical analysis and visualisation. It is particularly well established in epidemiology, public health and clinical research.

Like R, Stata is primarily command-based. Researchers can type commands to perform analyses and save those commands in a do-file. At the same time, Stata also provides a graphical interface, so many tasks can be performed through menus.

This combination makes Stata somewhat different from both fully menu-driven software and programming environments such as R. It provides the flexibility of a command-based workflow while remaining relatively approachable for researchers who are not experienced programmers.

Strengths

One of Stata's major strengths is its combination of statistical power and ease of use.

It can perform a wide range of commonly used analyses, including descriptive statistics, hypothesis tests, linear and logistic regression, survival analysis and many other methods used in epidemiological and clinical research.

Stata is also particularly good at data management. Researchers can easily create, modify, recode and transform variables. This is important because preparing the dataset is often a substantial part of a research project.

Another strength is its relatively consistent command structure. Once a researcher learns the basic syntax, the same approach can be used for many different analyses. Commands can also be combined to create more complicated workflows.

Stata also has good tools for data visualisation. Its graphics may not offer the same degree of customisation as R, but it can produce clear and publication-quality figures for many common applications.

Limitations

The most obvious limitation of Stata is that it is commercial software. A licence is required, although students and academic institutions may have access through their university or research organisation.

Stata is also less flexible than R in some areas. R has an enormous ecosystem of packages covering highly specialised methods, while Stata has a more controlled set of built-in procedures and user-contributed commands.

The command-based nature of Stata can also be a barrier for someone who has never used statistical software before. Although the graphical interface makes it easier to get started, researchers who want to take full advantage of Stata will eventually benefit from learning its commands.

Where is it commonly used?

Stata has a particularly strong presence in epidemiology and public health research. It is also widely used in clinical research and other areas where researchers work with observational or longitudinal data.

It is well suited to studies involving regression modelling, survival analysis and other common methods in epidemiology. It is also frequently encountered in academic research environments where researchers need to manage and analyse relatively large datasets.

Because Stata has been used extensively in epidemiological research, there is also a large body of published research, teaching material and examples available to researchers.

Reproducibility and verifiability

Stata supports reproducible and verifiable analysis through its command-based workflow.

Commands can be saved in do-files, which provide a record of the steps used to clean the data and perform the analysis. The same do-file can then be run again when the analysis needs to be repeated.

This is useful when a dataset is updated, when an error is discovered or when another researcher needs to check the analysis. Instead of relying on a record of menu selections, the researcher can examine the commands that produced the results.

Stata also allows researchers to document data management and statistical analysis within the same workflow. This can make it easier to understand how the original dataset was transformed into the final results.

As with any software, however, reproducibility depends on proper documentation. The commands need to be saved and the relevant data, software version and other important details should also be preserved.

Who should consider using it?

Stata is a particularly good choice for researchers working in epidemiology, public health and clinical research who want a powerful statistical package without the steeper learning curve of a general programming environment.

It is especially attractive to researchers who want a command-based and reproducible workflow but do not need the almost unlimited flexibility offered by R.

For a researcher who has access to a Stata licence and expects to perform regression, survival analysis or other common epidemiological analyses regularly, Stata can be an excellent choice.

In short, Stata offers a useful balance between power, usability and reproducibility. It may not be as flexible as R or as specialised for certain industries as SAS, but it is a very capable tool for many types of healthcare research.

SPSS

SPSS, originally called the Statistical Package for the Social Sciences, is a commercial statistical software package that has been widely used in academic and healthcare research. It is particularly popular among researchers who prefer a graphical interface rather than writing statistical code.

Most analyses in SPSS can be performed through menus and dialogue boxes. The researcher selects the variables, chooses the statistical test and specifies the required options. SPSS then produces the results in an output window.

This makes SPSS relatively easy to approach for someone who is new to statistical software. At the same time, it also has a syntax language that allows analyses to be written as commands rather than performed entirely through menus.

Strengths

The main strength of SPSS is its ease of use.

A researcher can perform many common statistical analyses without having to learn programming. Descriptive statistics, t-tests, chi-square tests, correlation, analysis of variance and several types of regression can be performed through the graphical interface.

SPSS is also reasonably straightforward for managing datasets. Variables can be defined and labelled, data can be recoded and transformed and missing values can be handled through the interface.

Another advantage is that the output is presented in a structured format. Tables containing statistical results are generated automatically, which can make it easier for beginners to understand the output of an analysis.

SPSS is also widely used in universities and research institutions. As a result, many researchers can find colleagues, teachers or supervisors who are already familiar with it.

Limitations

The main limitation of SPSS is that its simplicity comes at the cost of flexibility.

SPSS can perform a large number of statistical analyses, but researchers who need highly specialised or customised methods may find it restrictive compared with R. R has a much larger ecosystem of packages and allows researchers to customise almost every part of an analysis.

SPSS is also commercial software and requires a licence. Access may be available through an academic institution, but this can still be a limitation for independent researchers or organisations without an institutional licence.

Another issue is that researchers can become dependent on the graphical interface. Although SPSS has a syntax language, analyses performed entirely through menus may be harder to reproduce or verify later if the exact choices made by the researcher were not recorded.

Where is it commonly used?

SPSS is widely used in academic and healthcare research, particularly for studies involving standard statistical analyses.

It is commonly encountered in clinical research, surveys, questionnaire-based studies, psychology, social sciences and health services research.

For many studies involving relatively straightforward comparisons, associations and regression analyses, SPSS provides all the statistical methods that a researcher needs.

It is therefore particularly common among researchers who need to perform statistical analysis but do not work primarily as statisticians or data scientists.

Reproducibility and verifiability

SPSS can support reproducible and verifiable analysis, but how well it does so depends on how it is used.

If an analysis is performed entirely through menus, it may be difficult to reconstruct exactly what was done later. The output shows the results, but it may not always provide a complete record of every choice made during the analysis.

This problem can be reduced by using SPSS syntax. Instead of relying only on menu selections, researchers can save the commands used to perform the analysis. The syntax can then be run again on the same dataset or on an updated dataset.

Saving syntax also makes it easier for another researcher to inspect the analysis and verify how the results were obtained.

Therefore, although SPSS is often thought of as a point-and-click program, researchers doing serious research should consider using and saving syntax whenever possible. This makes the workflow much more reproducible and verifiable.

Who should consider using it?

SPSS is a good choice for researchers who want a relatively easy-to-use statistical package for standard analyses and do not want to spend a lot of time learning programming.

It can be particularly suitable for students and researchers conducting clinical, survey or health services research where the required statistical methods are relatively conventional.

However, researchers who expect to perform complex or highly customised analyses may eventually find R or Stata more suitable.

In short, SPSS is accessible, familiar and capable of handling many common healthcare research analyses. Its main advantage is ease of use, while its main limitation is that it offers less flexibility than more programming-oriented statistical environments.

SAS

SAS is a commercial software platform for data management and statistical analysis. It has been used for decades in research and is particularly well established in the pharmaceutical industry, clinical trials and other regulated research environments.

Unlike SPSS, SAS is primarily command-based. Researchers write programs that tell SAS how to import, clean and analyse the data. This makes it more similar to R and Stata in its general approach, although the programming language and workflow are different.

SAS is designed to handle large datasets and complex analytical workflows. It includes procedures for a wide range of statistical methods as well as tools for data management, reporting and visualisation.

Strengths

One of the major strengths of SAS is its ability to handle large datasets and complex workflows.

It provides a broad range of statistical procedures, including regression, survival analysis, longitudinal analysis and many methods used in clinical research. It also has strong capabilities for data manipulation and management.

SAS has a particularly strong position in clinical trials and pharmaceutical research. Its long history in these areas and its established procedures and documentation have made it an important tool in regulated research.

Another strength is the structured nature of SAS programming. A researcher can create programs that import the data, perform data transformations, conduct the statistical analysis and generate reports. This allows large parts of the research workflow to be automated.

SAS is also supported by extensive documentation and an established user community. For researchers working in environments where SAS is already part of the workflow, this can make collaboration and standardisation easier.

Limitations

The main limitations of SAS are its cost and learning curve.

SAS is commercial software and licences can be expensive, particularly for individual researchers or small research groups. Academic institutions and large organisations may already have access to it, but this is not always the case.

SAS programming can also be difficult for beginners. Researchers who are familiar with menu-driven software may initially find it less approachable.

Another consideration is that SAS may be more software than is necessary for a small research project involving only a few standard statistical tests. If the analysis is simple, a less complex and less expensive tool may be more practical.

SAS also has a large and mature ecosystem, but it does not have the same open-source package environment as R. Researchers looking for a newly developed or highly specialised statistical method may therefore find R more flexible.

Where is it commonly used?

SAS is particularly common in clinical trials, pharmaceutical research and other regulated healthcare research.

It is widely used for managing clinical trial data and performing statistical analyses required during the development and evaluation of medicines. It is also used in epidemiology, health services research and other areas where large datasets and complex analytical workflows are involved.

Its importance in clinical trials is partly related to the need for well-documented and standardised analytical processes. In such settings, the software is not chosen only because it can perform a statistical test. The entire process of data management, analysis and reporting needs to be carefully controlled and documented.

Reproducibility and verifiability

SAS supports reproducible and verifiable research through its programming-based workflow.

A SAS program can contain the commands used to import and prepare the data, perform the statistical analysis and generate the results. The program can be saved and run again when the analysis needs to be repeated.

This provides a clear record of how the results were produced. Another researcher can inspect the program and, given the appropriate data and environment, reproduce the analysis and verify the results.

This is particularly important in clinical trials and regulated research, where analytical procedures may need to be documented and reviewed carefully.

As with other programming-based software, reproducibility depends on more than saving the code. The data, software version, relevant procedures and other details of the analytical environment may also need to be documented.

Who should consider using it?

SAS is particularly suitable for researchers working in clinical trials, pharmaceutical research and large or regulated research environments, especially when SAS is already part of the organisation's workflow.

For an individual researcher conducting a small academic study with straightforward statistical analyses, SAS may be unnecessarily expensive and complex. In such cases, R, SPSS or Stata may be more practical depending on the requirements.

However, for researchers who work with large clinical datasets or plan to work in the pharmaceutical or clinical trial industry, learning SAS can be very valuable.

In short, SAS is powerful, reliable and well established in clinical trials and regulated research, but its cost and learning curve make it less attractive for some smaller or simpler research projects.

RevMan

RevMan, short for Review Manager, is a software platform developed by Cochrane specifically for systematic reviews and meta-analyses. This makes it different from other software in this list. It is not intended to be a general-purpose statistical package for analysing the data from a primary clinical study. Instead, it is designed around the workflow of evidence synthesis.

RevMan can be used to organise study information, enter or import data, perform meta-analyses and present the results. It also provides tools for producing common figures such as forest plots and funnel plots and for recording risk-of-bias assessments.

This makes RevMan particularly useful when the unit of analysis is not an individual patient in a new clinical study, but rather multiple existing studies that are being combined to answer a research question.

Strengths

The main strength of RevMan is its focus on systematic reviews and meta-analysis.

A researcher conducting a systematic review has to manage information from multiple studies, extract their results, assess risk of bias and, where appropriate, statistically combine their findings. RevMan brings many of these tasks into one environment.

RevMan can perform common pairwise meta-analyses and supports effect measures such as odds ratios, risk ratios, risk differences, mean differences and standardised mean differences. It also supports fixed-effect and random-effects models, subgroup analyses and sensitivity analyses.

Another major advantage is the generation of forest plots. A forest plot presents the effect estimate and confidence interval from each included study together with the overall meta-analytic estimate. RevMan can generate these plots directly from the analysis data.

RevMan also provides a structured environment for writing and managing reviews. This can be useful for researchers who are new to systematic reviews because the software is designed around the steps involved in conducting and reporting a review rather than requiring the researcher to build the entire workflow from scratch.

Limitations

The main limitation of RevMan is also its main strength: it is specialised software.

If you are analysing data from a primary clinical study, RevMan is not the tool you would choose. It is designed for systematic reviews and meta-analyses rather than general statistical analysis.

RevMan is also less flexible than R or Stata when highly customised or advanced statistical analyses are required. A researcher performing a complex meta-analysis may need to use a general statistical environment in addition to RevMan.

Another consideration is that RevMan has its own structured workflow. This is useful when conducting a systematic review, but researchers who want complete control over the statistical analysis may prefer a programming environment.

The availability and licensing arrangements should also be considered. RevMan is actively developed by Cochrane and is available through different access arrangements for Cochrane authors, individuals and organisations.

Where is it commonly used?

RevMan is most commonly encountered in systematic reviews and meta-analyses, particularly those following Cochrane methods.

It is useful when several clinical studies address the same question and their results need to be synthesised. For example, if several randomised trials have examined whether a particular treatment reduces mortality, a researcher may use RevMan to enter the results of the individual trials and perform a meta-analysis.

It is also useful for presenting the results of a review. Forest plots, risk-of-bias assessments and other elements can be incorporated into the review workflow.

RevMan is therefore best thought of as a systematic review and evidence-synthesis tool, rather than simply another alternative to R, SPSS or Stata.

Reproducibility and verifiability

RevMan provides a structured way to store study information, analysis data and review content. This can make the review easier to inspect and update.

The analysis is linked to the underlying study data, so changes to the data can be reflected in the analysis rather than requiring the researcher to manually recreate figures. RevMan also provides features for exporting data and maintaining review versions.

This is useful for verifiability because another researcher can examine the studies included in the review, the data entered for each study and the analyses performed.

However, reproducibility still depends on the quality of the systematic review itself. RevMan can perform a meta-analysis correctly according to the settings selected by the researcher, but it cannot decide whether the studies were selected appropriately, whether the studies are sufficiently similar to combine or whether the chosen statistical model is scientifically justified.

As with other statistical software, the software can perform the calculation, but the researcher remains responsible for the methodological decisions.

Who should consider using it?

RevMan is particularly suitable for researchers conducting systematic reviews and meta-analyses, especially those who want a relatively accessible environment for managing study data and performing standard meta-analyses.

It is a good option for someone who wants to perform a conventional pairwise meta-analysis without having to learn a general programming language.

However, researchers who need highly customised or advanced meta-analytic methods may benefit from using R or Stata instead, or using them alongside RevMan.

In short, RevMan is not a general-purpose statistical package. Its strength lies in doing one particular job well: helping researchers conduct, analyse and report systematic reviews and meta-analyses.

GraphPad Prism

GraphPad Prism is a commercial statistical software package designed primarily for scientific research, with particularly strong use in biomedical and laboratory research. It combines statistical analysis with data visualisation, making it possible to enter data, perform an analysis and produce a graph within the same program.

Unlike R, Stata and SAS, Prism does not require programming for its usual workflow. Researchers enter their data into tables designed for different types of analysis and then select the appropriate analysis from the menu.

Prism supports many commonly used statistical methods, including t-tests, ANOVA, correlation, linear and nonlinear regression, logistic regression and survival analysis. It also has specialised tools for dose-response curves, which are particularly useful in experimental and biomedical research.

Strengths

The main strength of GraphPad Prism is its simplicity and integration of statistics with graphing.

Prism is designed so that the structure of the data table guides the researcher towards appropriate analyses. For example, different tables are provided for data involving groups, two variables, categorical data or survival times.

It is particularly good for visualisation. Researchers can quickly create and customise graphs and can maintain a connection between the original data, the analysis and the resulting graph. If the underlying data or analysis is changed, the results and graphs can be updated automatically.

Prism is also particularly strong for nonlinear regression and curve fitting. It includes a large collection of predefined models and is widely used for applications such as dose-response curves, enzyme kinetics and growth or decay models.

Another advantage is that Prism provides guidance during statistical analysis. Its documentation and analysis interface include information about statistical assumptions and choosing appropriate tests. This can make it more approachable for researchers who are not statisticians.

Limitations

The main limitation of Prism is that it is not a general-purpose statistical programming environment.

It can perform many useful analyses, including some relatively advanced ones, but R, Stata and SAS generally offer greater flexibility for complex statistical modelling and specialised analyses.

Prism is also commercial software and therefore requires a licence. This may be less convenient for students or researchers who do not have institutional access.

Another limitation is that its simplicity can sometimes encourage researchers to focus on selecting a statistical test rather than thinking carefully about the study design and assumptions behind that test. Having a convenient menu does not guarantee that the selected analysis is appropriate.

This is an important point with any menu-driven statistical software. The software can make statistical analysis easier, but the researcher still needs to understand the underlying statistical principles.

Where is it commonly used?

GraphPad Prism is particularly common in biomedical and laboratory research.

It is frequently used for experimental studies where researchers need to compare groups, analyse dose-response relationships or examine relationships between measured variables.

For example, a laboratory researcher might use Prism to compare protein expression between treatment groups, plot a dose-response curve or analyse the growth of cells over time.

It is also useful for clinical and biological data when the statistical analysis is relatively straightforward and the presentation of the results is an important part of the workflow.

Reproducibility and verifiability

Prism provides some useful features for reproducibility because the data, analyses and graphs remain linked within a project. If the underlying data are changed, linked analyses and graphs can be updated automatically.

This is useful for checking an analysis and reducing errors caused by manually copying results from one program to another.

However, Prism is fundamentally different from R, Stata and SAS in this respect. Those programs allow the researcher to write a script that explicitly records the analytical steps. Prism's usual workflow is primarily graphical and does not require the researcher to write code.

Therefore, it may be less straightforward for another researcher to reconstruct every analytical decision from the project file alone, particularly when analyses have been performed through a series of graphical selections.

Prism can still be used in a reproducible workflow, especially when the original data, project files and analysis settings are preserved. But as with any software, the researcher should document the methods and statistical decisions rather than relying solely on the software to provide a record.

Who should consider using it?

GraphPad Prism is particularly suitable for researchers who want a simple way to analyse and graph biomedical or experimental data without learning programming.

It is a good choice for laboratory researchers and for clinical or biomedical researchers whose analyses mainly involve standard comparisons, regression, survival analysis or dose-response experiments.

Researchers working with large observational datasets or highly complex statistical models may find R, Stata or SAS more appropriate.

In short, GraphPad Prism is easy to use, particularly strong for visualisation and very useful for biomedical and experimental research. Its main advantage is convenience, while its main limitation is that it does not offer the same flexibility as a full statistical programming environment.

How Do They Compare?

By now, it should be clear that these six software packages are not direct competitors in every situation. They have different strengths and were developed with somewhat different users and research workflows in mind.

R, Stata, SPSS and SAS are general-purpose statistical tools, although they differ considerably in their flexibility and typical areas of use. RevMan is focused on systematic reviews and meta-analysis, while GraphPad Prism is particularly suited to biomedical and experimental research.

Cost and licensing are also important considerations. R is free and open-source. The other five require some form of paid access, although academic, student or institutional arrangements can make them considerably cheaper than standard commercial licences. For example, Stata currently offers discounted academic licences while RevMan has separate academic and student subscriptions.

A simple comparison is shown below.

Compasrison Table

The prices of commercial software are not always straightforward to compare. Some products have different editions, modules or subscription models. SPSS, for example, currently offers monthly, quarterly and annual subscriptions as well as perpetual and institutional licensing options. SAS uses several different licensing models depending on the product and type of organisation, so there is no single price that represents “SAS”. GraphPad Prism likewise has different plans for students, individuals and organisations.

RevMan is somewhat different. Individual annual subscriptions currently start at £130 plus VAT for the standard rate, £100 plus VAT for academics and £80 plus VAT for students in the UK and EU. Cochrane authors working on Cochrane Reviews can use RevMan Web without a subscription.

Which one is easiest to learn?

If ease of use is the main consideration, SPSS and GraphPad Prism are generally easier places to start. Both provide graphical interfaces that allow many common analyses to be performed without programming.

RevMan is also relatively approachable, particularly for researchers who are new to meta-analysis.

Stata requires some command-line knowledge but has a graphical interface as well. R and SAS generally require more familiarity with programming and therefore have a steeper learning curve.

However, ease of learning should not be the only consideration. A program may be easy to use but still be unsuitable for the analysis you need to perform.

Which one is most flexible?

R is the clear choice if flexibility is the main priority. Its large ecosystem of packages means that it can accommodate an enormous range of statistical methods and research workflows.

Stata and SAS are also powerful and can handle most analyses required in clinical and epidemiological research. SPSS can handle a large number of conventional analyses but is less flexible when highly specialised methods are required.

RevMan and GraphPad Prism are more specialised. This is not necessarily a weakness. Their focused design can make them easier to use when the research question falls within their intended purpose.

Which one is best for reproducibility?

R, Stata and SAS have a major advantage because they support a script-based workflow. The commands used to clean, analyse and present the data can be saved and rerun.

SPSS can also provide a reproducible workflow when researchers use and save its syntax rather than relying entirely on menus.

RevMan provides a structured project-based workflow for systematic reviews, while Prism keeps data, analyses and graphs linked within its projects. Both can therefore support reproducible research, although they do not provide the same code-based workflow as R, Stata or SAS.

In all cases, however, reproducibility depends on the researcher. Simply using a particular software package does not automatically make an analysis reproducible.

What about cost?

If cost is the main consideration, R is the obvious choice because the software itself is free and open-source.

The commercial packages require a licence, but the actual cost can vary considerably depending on whether you are a student, an academic researcher or part of a larger institution.

Stata, for example, has academic single-user licences at substantially reduced prices. SPSS offers student and academic licensing as well as commercial subscriptions and institutional licences. SAS is generally more expensive and its pricing depends heavily on the product and licensing model.

Therefore, researchers should check whether their university, hospital or research institution already provides access before purchasing software themselves.

Which one should you choose?

There is no single answer.

If you are a beginner performing relatively straightforward clinical research, SPSS may be a practical choice.

If you are working in epidemiology or public health, Stata or R are both strong choices.

If you want maximum flexibility and are willing to learn programming, R is difficult to beat.

If you are planning to work in clinical trials or the pharmaceutical industry, learning SAS can be particularly valuable.

If you are conducting a systematic review or meta-analysis, RevMan may be the most convenient starting point.

If you are doing laboratory or experimental biomedical research and want an easy way to analyse and present your data, GraphPad Prism can be very useful.

Ultimately, the software is only a tool. A well-designed study analysed with simple software is much more valuable than a poorly designed study analysed with the most sophisticated statistical package available.

Why I Use R

After discussing several statistical software packages, it is reasonable to ask which one I actually use. My preference is R. This is not because R is the easiest software to learn or because it is necessarily the best choice for every researcher. I use it because it gives me a combination of flexibility, programmability, extensibility, reproducibility and freedom that I find particularly valuable in research.

Flexibility

The biggest reason I use R is its flexibility.

Healthcare research rarely stays within a fixed set of statistical tests. A simple project may begin with descriptive statistics and a comparison between two groups, but eventually require regression, survival analysis, visualisation or a more specialised method.

R can accommodate all of these within the same environment. I do not have to change software every time the analysis becomes more complicated.

This flexibility is particularly useful when exploring a dataset. I can clean the data, examine distributions, create graphs, perform statistical analyses and build more complicated models within the same workflow.

Of course, flexibility also means that R gives the researcher many choices. That can be confusing for a beginner. But once the basics are understood, I find this freedom much more useful than restrictive.

Programmability

R is a programming language, and this is one of the things I value most about it.

Instead of telling the software what to do by repeatedly clicking through menus, I can write commands that describe the analysis. The commands can be saved, modified and reused.

This becomes particularly useful when the analysis involves many steps. For example, I can write a script that imports a dataset, cleans the variables, performs several analyses and produces the required tables and figures. If something changes, I can modify the relevant part of the script and run it again.

Programming also makes it easier to automate repetitive tasks. If I need to perform the same analysis on several datasets or generate similar figures for multiple variables, I do not have to repeat the same sequence of clicks.

For me, this changes statistical analysis from a series of isolated calculations into a reproducible workflow.

Almost unlimited extensibility

Another major advantage of R is its extensibility.

R itself is already powerful, but its capabilities can be extended through packages. Researchers around the world have developed packages for statistical methods, data manipulation, visualisation, epidemiology, survival analysis, meta-analysis and many other areas.

This means that when I encounter a new research problem, I am not restricted to the functions that were included in the original software. There may already be a package that provides the method I need.

The same extensibility applies beyond statistics. R can work with many different types of data and can interact with other tools and programming languages.

This creates an ecosystem rather than simply a statistical program.

There is, however, a caveat. A large ecosystem also means that researchers need to be careful about which packages they use. A package being available does not automatically mean that its method is appropriate, well maintained or correctly implemented for a particular research question.

Reproducibility

Reproducibility is another major reason I prefer R.

I can save the entire analysis as code. The same code can then be run again on the same data to reproduce the results or on updated data to generate an updated analysis.

This is particularly useful when a research project changes over time. Data may be corrected, additional participants may be included or the analysis may need to be modified. With a well-written script, these changes do not require starting the analysis from the beginning.

It also makes collaboration easier. Instead of sending someone a collection of tables and figures and explaining how they were produced, I can share the code that generated them.

This provides both reproducibility and verifiability. Another researcher can examine the code, follow the analytical steps and check how the reported results were obtained.

Of course, reproducibility is not automatic. The code needs to be properly written and documented, and the relevant data, packages and software versions may also need to be preserved. But R provides the tools to build such a workflow.

Open source

Finally, R is open-source software.

There is no licence fee for using R, and anyone can inspect, modify and distribute the source code according to its licence. This makes R accessible to students, researchers and institutions regardless of their ability to purchase commercial statistical software.

The open-source nature of R also contributes to its large ecosystem. Researchers can develop packages and share them with the wider community without waiting for a commercial vendor to add a particular feature.

For research, I find this particularly appealing. Statistical methods should ideally be accessible to researchers who need them rather than being limited by the ability to purchase a particular software licence.

My own workflow

The combination of flexibility, programmability, extensibility, reproducibility and open-source access is why R is my preferred statistical software for healthcare research.

I use R in a rather simple way. I do not use a graphical interface at all. I write my analyses as R script files and run them from the GNU/Linux command line. The script contains the complete workflow, from importing and cleaning the data to performing the analysis and generating the tables and figures. I can then run the same script whenever I need to repeat the analysis, without having to reproduce a sequence of clicks in a graphical interface.

This approach fits particularly well with why I prefer R in the first place. The analysis is represented as plain text, which makes it easy to inspect, edit, version and share. It also means that the statistical analysis can be treated like any other piece of reproducible research code. For me, the combination of R scripts, the GNU/Linux command line and an open-source software environment provides a simple and transparent workflow with very little dependence on a particular graphical interface.

Graphical User Interfaces for R

One of the main difficulties of learning R is that it requires programming. This is true, but it does not mean that R has to be used from a plain command line.

There are several graphical user interfaces (GUIs) and integrated development environments (IDEs) that make working with R easier. These range from tools that simply provide an easier environment for writing R code to applications that allow statistical analysis to be performed almost entirely through menus.

There is also another possibility that makes R particularly interesting: building your own graphical interface. With Shiny, researchers can create interactive web applications in R, allowing users to interact with statistical analyses without having to write R code themselves.

This distinction is important. A GUI does not necessarily make R a different statistical software. In most cases, it is simply a different way of interacting with the R ecosystem.

RStudio

For most people learning R, RStudio is probably the best place to start.

RStudio is an IDE designed specifically to make working with R easier. It provides a script editor, console, file browser, package management, plotting tools, debugging tools and other features in one interface. The open-source desktop edition is free. (posit.co)

However, RStudio is not really a point-and-click alternative to R. You still write R code. Its main advantage is that it makes writing, organising and running that code much more convenient.

R Commander

R Commander takes a different approach. It provides menus and dialogue boxes for many common statistical procedures. It can also display the R commands generated by those actions, allowing the researcher to see what is happening underneath the GUI. (R Commander)

This makes R Commander useful for someone who wants to learn R gradually. A beginner can start with menus and then examine the corresponding commands.

Its disadvantage is that it does not expose the full flexibility of R through the graphical interface. Eventually, a researcher who needs more specialised analyses will probably need to write R code directly.

jamovi

jamovi is another interesting option. It is built on R but presents statistical analysis through a modern graphical interface. It is particularly attractive to researchers who are accustomed to SPSS and want a similar point-and-click experience while benefiting from the R ecosystem.

One particularly useful feature is its R Syntax Mode. jamovi can show the R code corresponding to an analysis and that code can then be copied into an R session. The analyses available in jamovi are also provided through the jmv R package. (jamovi and R)

This makes jamovi more than just a GUI. It can serve as a bridge between traditional menu-driven statistics and R programming.

Another advantage is that jamovi saves the data, analyses, options and results together in a project file, which can make the workflow convenient to share and reproduce.

JASP

JASP follows a similar philosophy. It is a graphical statistical application designed to make statistical analysis accessible without requiring users to write code. (JASP)

One of JASP's particular strengths is Bayesian statistics. It provides an accessible graphical interface for Bayesian analyses and allows researchers to perform many Bayesian tests and models without having to write R code themselves. This makes it especially attractive to researchers who want to explore Bayesian methods but are not yet comfortable implementing them directly in R.

JASP also supports conventional frequentist analyses, so it can be used for both approaches within the same interface. It is free and open-source.

JASP can therefore be particularly useful as an introduction to Bayesian statistics. A researcher can focus on understanding concepts such as prior distributions, posterior distributions and Bayes factors without first having to learn the programming required to implement these analyses in R.

However, I would not describe JASP as simply another graphical interface for R in the same sense as RStudio. Although it uses R for some of its statistical functionality and has mechanisms for displaying or working with R code, the normal workflow is through JASP's own graphical interface.

Shiny

Shiny is different from the other tools discussed here. It is not primarily an interface for using R. Instead, it is a framework that allows you to build your own interactive web applications using R.

This can be extremely useful in healthcare research.

For example, a researcher could build a Shiny application where the user uploads a dataset, selects variables and chooses an analysis from a set of predefined options. The application can then perform the analysis and display tables and graphs without requiring the user to know any R.

Shiny therefore allows the researcher to separate the statistical engine from the user interface. The statistical analysis can be written in R while the end user interacts with a web-based application.

This can be useful when a statistical tool needs to be shared with clinicians or researchers who are not familiar with programming. It can also be used to build calculators, dashboards, interactive visualisations and specialised research tools.

The limitation is that creating a good Shiny application requires programming knowledge. It is therefore not really a solution for someone who wants to avoid programming. Instead, it is a way for an R programmer to create a user-friendly interface for other people.

How do they compare?

The easiest way to think about these programs is by asking how much programming you want to do.

There are also other interfaces, including RKWard and various specialised graphical tools. The R ecosystem is large enough that it is difficult to say that one interface is best for everyone.

Which one should you use?

For someone who wants to learn R properly, I would recommend RStudio. It does not hide the programming language. Instead, it makes programming easier. This means that the researcher can start with simple commands and gradually learn more without having to change software later.

For someone who wants to perform conventional statistical analyses without learning programming, jamovi or JASP may be more comfortable.

R Commander sits somewhere between the two. It provides a traditional statistical GUI while exposing the R commands behind the analysis, making it useful as a teaching tool.

If the goal is to build a statistical tool for other people, rather than simply analyse data yourself, Shiny becomes particularly interesting. It allows the researcher to turn an R analysis into an interactive application that can be used by people who have no knowledge of R.

The important point is that using a GUI does not remove the need to understand statistics. A menu can make it easier to run a regression, but it cannot tell you whether regression is appropriate for your study. The researcher still needs to understand the assumptions, interpret the results and decide whether the analysis answers the research question.

Conclusion

There is no universally best statistical software for healthcare research. Each tool has its own strengths and is suited to different types of research.

Choose the software based on your research question, the analysis you need and the workflow you are comfortable with. More importantly, learn the statistics rather than simply learning the software.

For me, R provides the best combination of flexibility, programmability, reproducibility and freedom, which is why it remains my preferred choice.

Links

• The R Project for Statistical Computing
• Stata — Statistical software for data science
• IBM SPSS software
• SAS
• RevMan
• GraphPad Prism