I love research but I hate statistics. I mean, doctor is a job complicated enough, I can’t become statistician on top of it, right? Now with R++, I love research. No buts.
This is from one of our first clients (cardiac surgeon at the CHU Purpan - Toulouse) and sums up very well the objective of R++: it lets doctors make their statistics simply, independently and above all, it reduces the pain.
The importance of methodology and statistical analysis in medical research cannot be overemphasized. You have collected data. Cleaning that data is an essential step because it is highly likely it contains outliers. Then you have a hypothesis in mind. So you need to do a statistical test to support it. The right test must be chosen from among the many tests that exist. Furthermore, to control biases and adjust for confounding factors, it will require modeling: Survival curves, regression. These and many other tasks can be complicated or impossible without good statistical analysis software. R++ was designed to do the job.
R++ is distinguished from other statistical analysis software packages in two fundamental ways: focus on medicine and its simple intuitive interface.
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Christophe Genolini, founder of R++, was a statistics researcher. He worked in the Inserm International team U669 (led by Bruno Falissard). He regularly gave statistical help to doctors in difficulty. Through these years of collaboration he has made two observations:
Hence the idea of creating R++ to answer those "classic" statistical questions in medical research, with an ergonomic interface so that doctors could be autonomous: R++.
The different disciplinary fields that use statistics do not use the same tools. Many software that try to address everything, often turned out to be difficult for doctors to use effectively. For example, in industry, measurements units are often in millisecond, but in medicine, the day is enough in the majority of cases. In HR, ages by gender are represented as an age pyramid, but in medicine, boxplot are preferred. In business the "inventory forecast" (so as not to have too much or too little of an item in inventory) is important, but is not used in medical research. Conversely, only doctors use ROC curves or survival models. So, R++ derives simplicity for doctors by its specific focus on medical research. We decided to focus on the tools that doctors need, such as boxplot, ROC and Survival, no age pyramid, no forecast. It keeps R++ simple.
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.
Following our requests, R++ provided us with a tool to generate Kaplan-Meier curves.
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