To design R++ we go to meet doctors to learn about what problems they have doing statistics, and then address those problems. Some of these were: "I had trouble finding the outliers"; "I never know which test to choose"; "I spend a lot of time editing my charts". For each problem, we work with the doctor to find and optimize a solution. After validation, the solutions are transformed into functionalities and made available to our users.
A statistical analysis usually takes place in 4 steps, and R++ walks you through these 4 steps to produce quality statistics, and publication. First, you need to clean up your data. There are always data outliers (such as, one patient entered height in centimeters instead of meters; one intern noted "Man/Female" while another noted "M/F", etc.). After cleaning the data, we look at whether there are links between the variables. This is done via statistical tests, and it is not always easy to know which test is the right one, so R++ assists. In the third step, one can then proceed to modeling: linear and logistic regression, ROC curves, Survival model. Finally, in the fourth step we export the results to the article.
For each of these steps, R++ has specific features to simplify your life and move you more efficiently toward your ultimate goal of data analysis, deriving results, and publishing.
Whether entered by doctors, interns, or directly by patients, whether they are single or multicenter, prospective or retrospective, all databases contain outliers, poorly coded variables or typing errors. R++ has developed a series of tools dedicated to the correction of your bases. They allow you to clean, but also to do univariate analysis and enrichment of your data such as the creation of new columns. In this short video, we show you how to:
Statistical tests are the heart of many articles or dissertations. Statistics provide the “p” so appreciated by reviewers. You know a lot of them: khi2, Student's t, Pearson or Spearman correlation, rank tests. But, it is not always easy to know which test to use and under what conditions. So R++ remembers it for you. In a few clicks, R++ gives you all the tests related to the variable you are interested in. It gives you not only the tests, but also the graphs, the density, the bivariate summaries. And if, despite everything, some doubt remains, “Help” is there to enlighten you. In the video you can find:
Statistical tests make it possible to establish the existence of a link between two variables. But to go further to control bias or to establish interactions between more than two variables, you need to model. R++ gives you access to the 5 most used models in medicine: linear regression, logistic regression, multivariate ANOVA, ROC curves and Survival model (Log-rank, Cox). Just click on the variables that interest you and your model is built automatically on the fly, in real time.
R++ can be fun. But when you do statistics, you usually aim to publish an article. R++ is compatible with the Microsoft suite. Just drag & drop your graphs into Word. You can also copy and paste tables into Excel or Powerpoint.
Many journals ask that the first table “Table 1” of your article be a comparison of the characteristics of your intervention group and your control group (such as there are this many women in each group and the average age is x). It is not very complicated to build, but you have to calculate one by one many averages, standard deviations, numbers. This is particularly time-consuming. R++ includes a Table 1 editor. In R++, your Table 1 is built in a few clicks ... and it is ready for your article.
Whether you are CHU, GHT, doctor, or private institution, we have offers according to your needs and budget!
Find the formula that suits you best with scalable rates adapted to your situation.
The thing we appreciate most about R++ analysis software is having a team that is very attentive to our requests.
I am bad at stats, I hate stats. R++, it (almost) made me love stats.
It's easy to use and fun. It saves you a lot of time. Do not hesitate!
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