At R++, we work directly with doctors to understand the real challenges they face in statistical analysis, then we build solutions to tackle them.
"I had trouble finding the outliers."
"I never know which test to choose."
"I spend too much time editing my charts."
Sound familiar? For each issue, we collaborate with medical professionals to develop a solution, which we then integrate into R++, making new features available to all our users.
Statistical analysis for medical research typically follows four key steps. R++ streamlines each one, ensuring high-quality stats and a smoother path to getting published.
First up, data cleaning. Real-world data is rarely perfect. Patients might enter height in centimetres instead of metres, or gender may be recorded inconsistently (e.g., “Male/Female” vs “M/F”). R++ simplifies data cleaning, making errors easy to spot and correct outliers.
Then it’s time for statistical testing. Determining the right test for your variables can be complex, but again R++ steps in to guide your choice.
Once your data is clean, you can move on to modelling. R++ provides the tools you need to build robust, insightful models for linear or logistic regression, ROC curves, or survival analysis.
Finally, when it comes to writing your article, R++ seamlessly integrates your results tables and graphs with a simple copy and paste or drag and drop.
Before diving into statistical analysis, ensuring your data is clean and structured is essential. Whether your data has been entered by doctors, students, or patients, whether it’s single-centre or multi-centre, prospective or retrospective, every dataset contains outliers, coding inconsistencies, and typos.
R++ simplifies data cleaning with a suite of intuitive tools designed to correct, enrich, and prepare your dataset for analysis. In this short video, we’ll show you how to:
Statistical tests are the backbone of many medical articles and dissertations, providing the all-important “p-value” that reviewers scrutinise. Whether you're familiar with tests like Chi-Square (χ²), Student's t-Test, Pearson or Spearman correlation, or rank-based tests, it’s not always clear which test to use or under what circumstances. That’s where R++ steps in to make life easier.
In just a few clicks, R++ not only recommends the right statistical tests for your variables, but also provides the corresponding graphs, density plots, and bivariate summaries. If there’s ever any uncertainty, R++ has a built-in help feature to guide you through the process.
In this video, you’ll see how to use R++ for the following statistical tests:
Statistical tests can identify basic relationships between two variables, but to take your analysis further, controlling for biases, or exploring interactions between multiple variables, you need to build models.
R++ gives you access to the 5 most used models in medical research. Whether you're running linear regression, logistic regression, multivariate ANOVA, ROC curves, or survival models (Log-rank, Cox), R++ simplifies the process. Simply select your variables, and your model is generated automatically in real-time.
R++ can help you with:
Whether you're preparing for a journal submission or a conference presentation, R++ makes it easy to create publication-quality graphs.
With just a few clicks, you can:
In medical research, Table 1 is a crucial part of any article, providing a comparison between the intervention group and the control group. It typically includes averages, standard deviations, and counts which take time to calculate and organised. With R++’s Table 1 editor, this tedious task becomes far more efficient.
Whether you're a doctor, part of an independent practice, or working in a public sector healthcare facility, we offer flexible plans designed to suit your needs and budget.
Find the perfect plan for you with flexible pricing options designed to meet your unique requirements.
The best thing about R++ is having a team that genuinely listens to our needs
I'm bad at stats, I hate stats. R++ (almost) made me enjoy stats!
It's intuitive, enjoyable to use and has helped save me valuable time. Give it a go!
Want to find out how Healthcare Informatics (HCI) can revolutionise statistical analysis?
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